Last Updated on July 12, 2019

Generative Adversarial Networks, or GANs, are challenging to train.

This is because the architecture involves both a generator and a discriminator model that compete in a zero-sum game. It means that improvements to one model come at the cost of a degrading of performance in the other model. The result is a very unstable training process that can often lead to failure, e.g. a generator that generates the same image all the time or generates nonsense.

As such, there are a number of heuristics or best practices (called “*GAN hacks*“) that can be used when configuring and training your GAN models. These heuristics are been hard won by practitioners testing and evaluating hundreds or thousands of combinations of configuration operations on a range of problems over many years.

Some of these heuristics can be challenging to implement, especially for beginners.

Further, some or all of them may be required for a given project, although it may not be clear which subset of heuristics should be adopted, requiring experimentation. This means a practitioner must be ready to implement a given heuristic with little notice.

In this tutorial, you will discover how to implement a suite of best practices or GAN hacks that you can copy-and-paste directly into your GAN project.

After reading this tutorial, you will know:

- The best sources for practical heuristics or hacks when developing generative adversarial networks.
- How to implement seven best practices for the deep convolutional GAN model architecture from scratch.
- How to implement four additional best practices from Soumith Chintala’s GAN Hacks presentation and list.

**Kick-start your project** with my new book Generative Adversarial Networks with Python, including *step-by-step tutorials* and the *Python source code* files for all examples.

Let’s get started.

## Tutorial Overview

This tutorial is divided into three parts; they are:

- Heuristics for Training Stable GANs
- Best Practices for Deep Convolutional GANs
- Downsample Using Strided Convolutions
- Upsample Using Strided Convolutions
- Use LeakyReLU
- Use Batch Normalization
- Use Gaussian Weight Initialization
- Use Adam Stochastic Gradient Descent
- Scale Images to the Range [-1,1]

- Soumith Chintala’s GAN Hacks
- Use a Gaussian Latent Space
- Separate Batches of Real and Fake Images
- Use Label Smoothing
- Use Noisy Labels

## Heuristics for Training Stable GANs

GANs are difficult to train.

At the time of writing, there is no good theoretical foundation as to how to design and train GAN models, but there is established literature of heuristics, or “*hacks*,” that have been empirically demonstrated to work well in practice.

As such, there are a range of best practices to consider and implement when developing a GAN model.

Perhaps the two most important sources of suggested configuration and training parameters are:

- Alec Radford, et al’s 2015 paper that introduced the DCGAN architecture.
- Soumith Chintala’s 2016 presentation and associated “
*GAN Hacks*” list.

In this tutorial, we will explore how to implement the most important best practices from these two sources.

## Best Practices for Deep Convolutional GANs

Perhaps one of the most important steps forward in the design and training of stable GAN models was the 2015 paper by Alec Radford, et al. titled “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.”

In the paper, they describe the Deep Convolutional GAN, or DCGAN, approach to GAN development that has become the de facto standard.

We will look at how to implement seven best practices for the DCGAN model architecture in this section.

### 1. Downsample Using Strided Convolutions

The discriminator model is a standard convolutional neural network model that takes an image as input and must output a binary classification as to whether it is real or fake.

It is standard practice with deep convolutional networks to use pooling layers to downsample the input and feature maps with the depth of the network.

This is not recommended for the DCGAN, and instead, they recommend downsampling using strided convolutions.

This involves defining a convolutional layer as per normal, but instead of using the default two-dimensional stride of (1,1) to change it to (2,2). This has the effect of downsampling the input, specifically halving the width and height of the input, resulting in output feature maps with one quarter the area.

The example below demonstrates this with a single hidden convolutional layer that uses downsampling strided convolutions by setting the ‘*strides*‘ argument to (2,2). The effect is the model will downsample the input from 64×64 to 32×32.

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# example of downsampling with strided convolutions from keras.models import Sequential from keras.layers import Conv2D # define model model = Sequential() model.add(Conv2D(64, kernel_size=(3,3), strides=(2,2), padding='same', input_shape=(64,64,3))) # summarize model model.summary() |

Running the example shows the shape of the output of the convolutional layer, where the feature maps have one quarter of the area.

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_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 32, 32, 64) 1792 ================================================================= Total params: 1,792 Trainable params: 1,792 Non-trainable params: 0 _________________________________________________________________ |

### 2. Upsample Using Strided Convolutions

The generator model must generate an output image given as input at a random point from the latent space.

The recommended approach for achieving this is to use a transpose convolutional layer with a strided convolution. This is a special type of layer that performs the convolution operation in reverse. Intuitively, this means that setting a stride of 2×2 will have the opposite effect, upsampling the input instead of downsampling it in the case of a normal convolutional layer.

By stacking a transpose convolutional layer with strided convolutions, the generator model is able to scale a given input to the desired output dimensions.

The example below demonstrates this with a single hidden transpose convolutional layer that uses upsampling strided convolutions by setting the ‘*strides*‘ argument to (2,2).

The effect is the model will upsample the input from 64×64 to 128×128.

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# example of upsampling with strided convolutions from keras.models import Sequential from keras.layers import Conv2DTranspose # define model model = Sequential() model.add(Conv2DTranspose(64, kernel_size=(4,4), strides=(2,2), padding='same', input_shape=(64,64,3))) # summarize model model.summary() |

Running the example shows the shape of the output of the convolutional layer, where the feature maps have quadruple the area.

1 2 3 4 5 6 7 8 9 |
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_transpose_1 (Conv2DTr (None, 128, 128, 64) 3136 ================================================================= Total params: 3,136 Trainable params: 3,136 Non-trainable params: 0 _________________________________________________________________ |

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### 3. Use LeakyReLU

The rectified linear activation unit, or ReLU for short, is a simple calculation that returns the value provided as input directly, or the value 0.0 if the input is 0.0 or less.

It has become a best practice when developing deep convolutional neural networks generally.

The best practice for GANs is to use a variation of the ReLU that allows some values less than zero and learns where the cut-off should be in each node. This is called the leaky rectified linear activation unit, or LeakyReLU for short.

A negative slope can be specified for the LeakyReLU and the default value of 0.2 is recommended.

Originally, ReLU was recommend for use in the generator model and LeakyReLU was recommended for use in the discriminator model, although more recently, the LeakyReLU is recommended in both models.

The example below demonstrates using the LeakyReLU with the default slope of 0.2 after a convolutional layer in a discriminator model.

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# example of using leakyrelu in a discriminator model from keras.models import Sequential from keras.layers import Conv2D from keras.layers import BatchNormalization from keras.layers import LeakyReLU # define model model = Sequential() model.add(Conv2D(64, kernel_size=(3,3), strides=(2,2), padding='same', input_shape=(64,64,3))) model.add(LeakyReLU(0.2)) # summarize model model.summary() |

Running the example demonstrates the structure of the model with a single convolutional layer followed by the activation layer.

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_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 32, 32, 64) 1792 _________________________________________________________________ leaky_re_lu_1 (LeakyReLU) (None, 32, 32, 64) 0 ================================================================= Total params: 1,792 Trainable params: 1,792 Non-trainable params: 0 _________________________________________________________________ |

### 4. Use Batch Normalization

Batch normalization standardizes the activations from a prior layer to have a zero mean and unit variance. This has the effect of stabilizing the training process.

Batch normalization is used after the activation of convolution and transpose convolutional layers in the discriminator and generator models respectively.

It is added to the model after the hidden layer, but before the activation, such as LeakyReLU.

The example below demonstrates adding a Batch Normalization layer after a Conv2D layer in a discriminator model but before the activation.

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# example of using batch norm in a discriminator model from keras.models import Sequential from keras.layers import Conv2D from keras.layers import BatchNormalization from keras.layers import LeakyReLU # define model model = Sequential() model.add(Conv2D(64, kernel_size=(3,3), strides=(2,2), padding='same', input_shape=(64,64,3))) model.add(BatchNormalization()) model.add(LeakyReLU(0.2)) # summarize model model.summary() |

Running the example shows the desired usage of batch norm between the outputs of the convolutional layer and the activation function.

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_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 32, 32, 64) 1792 _________________________________________________________________ batch_normalization_1 (Batch (None, 32, 32, 64) 256 _________________________________________________________________ leaky_re_lu_1 (LeakyReLU) (None, 32, 32, 64) 0 ================================================================= Total params: 2,048 Trainable params: 1,920 Non-trainable params: 128 _________________________________________________________________ |

### 5. Use Gaussian Weight Initialization

Before a neural network can be trained, the model weights (parameters) must be initialized to small random variables.

The best practice for DCAGAN models reported in the paper is to initialize all weights using a zero-centered Gaussian distribution (the normal or bell-shaped distribution) with a standard deviation of 0.02.

The example below demonstrates defining a random Gaussian weight initializer with a mean of 0 and a standard deviation of 0.02 for use in a transpose convolutional layer in a generator model.

The same weight initializer instance could be used for each layer in a given model.

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# example of gaussian weight initialization in a generator model from keras.models import Sequential from keras.layers import Conv2DTranspose from keras.initializers import RandomNormal # define model model = Sequential() init = RandomNormal(mean=0.0, stddev=0.02) model.add(Conv2DTranspose(64, kernel_size=(4,4), strides=(2,2), padding='same', kernel_initializer=init, input_shape=(64,64,3))) |

### 6. Use Adam Stochastic Gradient Descent

Stochastic gradient descent, or SGD for short, is the standard algorithm used to optimize the weights of convolutional neural network models.

There are many variants of the training algorithm. The best practice for training DCGAN models is to use the Adam version of stochastic gradient descent with the learning rate of 0.0002 and the beta1 momentum value of 0.5 instead of the default of 0.9.

The Adam optimization algorithm with this configuration is recommended when both optimizing the discriminator and generator models.

The example below demonstrates configuring the Adam stochastic gradient descent optimization algorithm for training a discriminator model.

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# example of using adam when training a discriminator model from keras.models import Sequential from keras.layers import Conv2D from keras.optimizers import Adam # define model model = Sequential() model.add(Conv2D(64, kernel_size=(3,3), strides=(2,2), padding='same', input_shape=(64,64,3))) # compile model opt = Adam(lr=0.0002, beta_1=0.5) model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy']) |

### 7. Scale Images to the Range [-1,1]

It is recommended to use the hyperbolic tangent activation function as the output from the generator model.

As such, it is also recommended that real images used to train the discriminator are scaled so that their pixel values are in the range [-1,1]. This is so that the discriminator will always receive images as input, real and fake, that have pixel values in the same range.

Typically, image data is loaded as a NumPy array such that pixel values are 8-bit unsigned integer (uint8) values in the range [0, 255].

First, the array must be converted to floating point values, then rescaled to the required range.

The example below provides a function that will appropriately scale a NumPy array of loaded image data to the required range of [-1,1].

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# example of a function for scaling images # scale image data from [0,255] to [-1,1] def scale_images(images): # convert from unit8 to float32 images = images.astype('float32') # scale from [0,255] to [-1,1] images = (images - 127.5) / 127.5 return images |

## Soumith Chintala’s GAN Hacks

Soumith Chintala, one of the co-authors of the DCGAN paper, made a presentation at NIPS 2016 titled “How to Train a GAN?” summarizing many tips and tricks.

The video is available on YouTube and is highly recommended. A summary of the tips is also available as a GitHub repository titled “How to Train a GAN? Tips and tricks to make GANs work.”

The tips draw upon the suggestions from the DCGAN paper as well as elsewhere.

In this section, we will review how to implement four additional GAN best practices not covered in the previous section.

### 1. Use a Gaussian Latent Space

The latent space defines the shape and distribution of the input to the generator model used to generate new images.

The DCGAN recommends sampling from a uniform distribution, meaning that the shape of the latent space is a hypercube.

The more recent best practice is to sample from a standard Gaussian distribution, meaning that the shape of the latent space is a hypersphere, with a mean of zero and a standard deviation of one.

The example below demonstrates how to generate 500 random Gaussian points from a 100-dimensional latent space that can be used as input to a generator model; each point could be used to generate an image.

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# example of sampling from a gaussian latent space from numpy.random import randn # generate points in latent space as input for the generator def generate_latent_points(latent_dim, n_samples): # generate points in the latent space x_input = randn(latent_dim * n_samples) # reshape into a batch of inputs for the network x_input = x_input.reshape((n_samples, latent_dim)) return x_input # size of latent space n_dim = 100 # number of samples to generate n_samples = 500 # generate samples samples = generate_latent_points(n_dim, n_samples) # summarize print(samples.shape, samples.mean(), samples.std()) |

Running the example summarizes the generation of 500 points, each comprised of 100 random Gaussian values with a mean close to zero and a standard deviation close to 1, e.g. a standard Gaussian distribution.

1 |
(500, 100) -0.004791256735601787 0.9976912528950904 |

### 2. Separate Batches of Real and Fake Images

The discriminator model is trained using stochastic gradient descent with mini-batches.

The best practice is to update the discriminator with separate batches of real and fake images rather than combining real and fake images into a single batch.

This can be achieved by updating the model weights for the discriminator model with two separate calls to the train_on_batch() function.

The code snippet below demonstrates how you can do this within the inner loop of code when training your discriminator model.

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... # get randomly selected 'real' samples X_real, y_real = ... # update discriminator model weights discriminator.train_on_batch(X_real, y_real) # generate 'fake' examples X_fake, y_fake = ... # update discriminator model weights discriminator.train_on_batch(X_fake, y_fake) |

### 3. Use Label Smoothing

It is common to use the class label 1 to represent real images and class label 0 to represent fake images when training the discriminator model.

These are called hard labels, as the label values are precise or crisp.

It is a good practice to use soft labels, such as values slightly more or less than 1.0 or slightly more than 0.0 for real and fake images respectively, where the variation for each image is random.

This is often referred to as label smoothing and can have a regularizing effect when training the model.

The example below demonstrates defining 1,000 labels for the positive class (class=1) and smoothing the label values uniformly into the range [0.7,1.2] as recommended.

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# example of positive label smoothing from numpy import ones from numpy.random import random # example of smoothing class=1 to [0.7, 1.2] def smooth_positive_labels(y): return y - 0.3 + (random(y.shape) * 0.5) # generate 'real' class labels (1) n_samples = 1000 y = ones((n_samples, 1)) # smooth labels y = smooth_positive_labels(y) # summarize smooth labels print(y.shape, y.min(), y.max()) |

Running the example summarizes the min and max values for the smooth values, showing they are close to the expected values.

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(1000, 1) 0.7003103006957805 1.1997858934066357 |

There have been some suggestions that only positive-class label smoothing is required and to values less than 1.0. Nevertheless, you can also smooth negative class labels.

The example below demonstrates generating 1,000 labels for the negative class (class=0) and smoothing the label values uniformly into the range [0.0, 0.3] as recommended.

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# example of negative label smoothing from numpy import zeros from numpy.random import random # example of smoothing class=0 to [0.0, 0.3] def smooth_negative_labels(y): return y + random(y.shape) * 0.3 # generate 'fake' class labels (0) n_samples = 1000 y = zeros((n_samples, 1)) # smooth labels y = smooth_negative_labels(y) # summarize smooth labels print(y.shape, y.min(), y.max()) |

### 4. Use Noisy Labels

The labels used when training the discriminator model are always correct.

This means that fake images are always labeled with class 0 and real images are always labeled with class 1.

It is recommended to introduce some errors to these labels where some fake images are marked as real, and some real images are marked as fake.

If you are using separate batches to update the discriminator for real and fake images, this may mean randomly adding some fake images to the batch of real images, or randomly adding some real images to the batch of fake images.

If you are updating the discriminator with a combined batch of real and fake images, then this may involve randomly flipping the labels on some images.

The example below demonstrates this by creating 1,000 samples of real (class=1) labels and flipping them with a 5% probability, then doing the same with 1,000 samples of fake (class=0) labels.

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# example of noisy labels from numpy import ones from numpy import zeros from numpy.random import choice # randomly flip some labels def noisy_labels(y, p_flip): # determine the number of labels to flip n_select = int(p_flip * y.shape[0]) # choose labels to flip flip_ix = choice([i for i in range(y.shape[0])], size=n_select) # invert the labels in place y[flip_ix] = 1 - y[flip_ix] return y # generate 'real' class labels (1) n_samples = 1000 y = ones((n_samples, 1)) # flip labels with 5% probability y = noisy_labels(y, 0.05) # summarize labels print(y.sum()) # generate 'fake' class labels (0) y = zeros((n_samples, 1)) # flip labels with 5% probability y = noisy_labels(y, 0.05) # summarize labels print(y.sum()) |

Try running the example a few times.

The results show that approximately 50 “1”s are flipped to 1s for the positive labels (e.g. 5% of 1,0000) and approximately 50 “0”s are flopped to 1s in for the negative labels.

1 |
950.049.0 |

## Further Reading

This section provides more resources on the topic if you are looking to go deeper.

### Papers

- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015.
- Tutorial: Generative Adversarial Networks, NIPS, 2016.
- Improved Techniques for Training GANs, 2016.

### API

### Articles

- ganhacks: How to Train a GAN? Tips and tricks to make GANs work
- Ian Goodfellow, Introduction to GANs, NIPS 2016.
- Soumith Chintala, How to train a GAN, NIPS 2016 Workshop on Adversarial Training.

## Summary

In this tutorial, you discovered how to implement a suite of best practices or GAN hacks that you can copy-and-paste directly into your GAN project.

Specifically, you learned:

- The best sources for practical heuristics or hacks when developing generative adversarial networks.
- How to implement seven best practices for the deep convolutional GAN model architecture from scratch.
- How to implement four additional best practices from Soumith Chintala’s GAN Hacks presentation and list.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

sir is we apply GAN on numerical values used for classification instead of Images or GAN works on images

Most of the work is on using GANs for image data.

There is some use on other data, such as text and time series.

So why using GAN for classification. Suppose we want high classification accuracy, the discriminator is then forced to train on poorly generated images as we will want to make discriminator “win the game” in order to have better classification. Im missing something or this is a bit of computational waste? If we are not training the generator we are also not using a lot of generated data (that is supposed to be the point of using SGAN) as we want to force an early stop scenario right? So Is there no way of making a good classifier AND a good generator? Can you make a good classiier with a bad generator and not call it a computational waste?? Btw thanks again for your epic work!

GANs are appropriate for classification when all other models fail. They can be used for semi-supervised, when you have lots of unlabelled data and only a tiny amount of labelled data.

The key to GANs is the adversarial training of both models. It does not work when you train one model in isolation, e.g. just the generator or just the discriminator.

If you want to train a single model, you can train a classification model directly. And you should as a point of comparison and only use a GAN if it can perform better.

Such a helpful post! Applied them to the Generating Dog Images kaggle competition. Although I didn’t get perfect looking dogs, these GAN hacks helped improve significantly.

Thank you!

Well done, very cool!

Hey Jason,

I am wondering if there is a recommended batch size for performance improvements. Currently I compared 32 and 256. Batch size of 32 took longer to train and gave worse results than batch size 256. Is there a reasoning behind this?

Great question!

Smallish seems better on most problems, e.g. 32, 64.

Too much seems to cause one model to learn faster than the other, e.g. it becomes unstable and you get a failure mode.

I see, thank you. But does smaller mean longer training time for each epoch?

Yes.

Question on 3. Use Label Smoothing.

Won’t this affect the loss function if the value is above 1?

I am assuming something like cross-entropy will give different results?

As for Use Noisy Labels, any intution as to why this is better?

The idea is to manipulate how the loss function sees the problem, to bias it.

Hello Jason? Can I use GAN to classificariam?

You could use the discriminator as the basis for a discriminator via transfer learning.

Thank you very much.

You’re welcome.

Hi Jason,

GREAT article! I’m curious about the smoothing of positive class labels, using the structure in your e-book of the train() method. When applying this technique in the generate_real_samples() helper method, should the smoothing also be used in the train() method when creating inverted labels for the generator to encourage better output images?

It seems logical. Your thoughts?

Thanks for the help! (And your e-book is fantastic, btw!)

Good question.

Hmmm. Probably.

Perhaps try with and without and confirm.

I just ran it on a couple of experiments, and it seems to regularize a tad. Cool!

Nice work!

When I am applying label smoothing the accuracy of the discriminator on real samples is equal to zero. Any ideas why this is the case?

Ignore the accuracy and focus on the loss, specifically learning curves.

Also focus on the images that are periodically generated, say every few epochs.

Hi Jason, thank for the great article !

Is it recommended to use both label smoothing and noisy label?

Also, i read that Soumith (https://github.com/soumith/ganhacks) recommends using SGD for Discriminator and Adam for Generator. What is your thought on this?

Perhaps try it on your specific GAN model.

I have not found a need to use either.

Hi Jason, great blog as usual.

Can these principles be applied to training autoencoders as well? As in:

1)ReLU for encoder layers and LeakyReLU for decoder layers

2) ‘tanh’ activation function for the last layer of the decoder network etc.

Generally, no. The tips focus on GANs and how unstable the training process is.

Autoencoders are super stable to train.

Does a shallow discriminator network fare better than a deeper one in terms of mode collapse and stability ? E.g- the discriminator in your pix2pix post vs one with more layers.

Also, this post was very helpful. Thank you.

It really depends on the configuration of the model and the specifics of the dataset.

A deeper model worked in my case.

Also, if we flip labels, it would lead the discriminator to bias/be confused and thus, prevent the generator from creating the same set of features to fool the discriminator. Am I correct about this intuition behind flipping some of the labels (4. Use noisy labels) ?

A little noise is good – keeps the model learning, a lot of noise is confusing.

Hi Jason, Great tutorials!!!

I just acquired your book and i’m playing a little bit with the hacks you recommended. Running the complete code just as you delivered got the following learning curves (not pretty good):

https://drive.google.com/file/d/1C6rJP0NfynCpyV5THhfz_INphLH6E3Pn/view?usp=sharing

Then i changed it to use SGD on the discriminator and ReLU on the generator with no particular improvements:

https://drive.google.com/file/d/1uv-u8dePjApo8gC1N-bipfbUAwUbNfm_/view?usp=sharing

What got the best boost was using label smoothing and noisy labels (with previous changes):

1. Using only label smoothing

https://drive.google.com/file/d/1bbUkzODyF6m_qb1IinO_bt9dkRJWVEj5/view?usp=sharing

2. Using both Label smoothing and Noisy labels

https://drive.google.com/file/d/118b72LaAuCtq9zfmofIBGyPIXxUkb1Ke/view?usp=sharing

But still the generator loss keeps converging towards zero, making myself think that probably is fooling the discriminator with garbage somehow, is this possible?. I think the generated images of this last setting confirm my beliefs:

https://drive.google.com/file/d/1mCOpn7Vj7-PqERXY9tHnfIpSoLkyo4xU/view?usp=sharing

Even though you see something in the middle trying to be an eight it’s still super noisy in my opinion and even repetitive.

I’m still struggling to raise the generator loss over 1.0 as you suggest, any thoughts?

I think the discriminator is failing to detect fake samples more than it fails to detect real samples, that’s maybe why is easy for the generator to fool the discriminator with garbage, maybe applying noise only on one of the batches (real or fake) could help. I assume both discriminator losses should be kind of similar

Nice progress Juan!

Perhaps the models are too small/not powerful enough for your dataset?

Try scaling them up a lot, or taking working model architectures from another similar project as a starting point?

Perhaps try reducing your dataset (size or complexity) to see if you can get something started.

Try things to learn more about the cause of the difficulty.

Let me know how you go.

I forgot to said i’m using the MNIST (digit 8) dataset for all the experiments, sorry, i had some mistakes generating the noisy labels, but i corrected them and i got this result:

https://drive.google.com/file/d/1fTvUrWsaddvxM8gx6aM24gCVAkvF1arg/view?usp=sharing

Discriminator loss on fake samples is still way above the “acceptable value” for MNIST example, and generator still going towards zero, i will try to move and tune some hyperparameters, or maybe i will try to use a less complex number such as 1 to see if i can get better results

Thanks

Perhaps ignore accuracy for now, focus on loss and the quality of the generated images.

Great article!

I would like to know if the smooth and noisy labels should also be used for generated images labels when training the generator?

Noisy labels can help in some cases. Perhaps try it with your model and compare the results.