Master GANs from Scratch: Implement 11 Game-Changing Models

Analyze the original gan paper, comparing a generator and a discriminator in a minimax game, and learn how their adversarial training aligns generated data with real data.

Build and train a PyTorch GAN by implementing a two-layer MLP generator and a discriminator for MNIST 28x28 images, then assemble the training loop with data sampling and noise generation.

Maximize the generator’s gradient signal by using log(d(g(z))) instead of the standard binary cross entropy loss, boosting numerical stability and training dynamics in a compact gan implementation.

Implement a gaussian noise sampler with zero mean and identity covariance for CPU or CUDA, latent size 100, and a mini-batch loader flattening MNIST to 784 features for unsupervised learning.

Push the generator and discriminator to the device and configure SGD optimizers with learning rate 0.1 and momentum 0.5. Run the training loop and analyze the results with plots.

The lecture demonstrates results from two parallel GAN trainings, showing mode collapse, minor noise-driven deviations, and then plots 25 generated images in a 5x5 grid using z sampling.

Implement a simple gan with mlp generator and discriminator on mnist using pytorch, with -1 to 1 outputs, binary cross-entropy loss, and a training loop noting mode collapse risks.

Explore conditional gans that condition both generator and discriminator on class labels to model p(x|y) and enable multimodal outputs, including inpainting and tagging.

implement a conditional gan from scratch by adapting the generator and discriminator to accept context, using one-hot labels, and training with mnist data.

Modify helpers to sample uniform or gaussian noise and to return one-hot conditioned labels, then integrate Y into the training loop and add a learning-rate scheduler.

implement exponential learning rate schedulers in pytorch to decay the generator and discriminator optimizers at every iteration, and stop when the target learning rate is reached.

Evaluate a GAN by generating conditioned samples across ten labels in a 10x10 grid. Use one-hot context vectors and device handling, and discuss results, sensitivity to hyperparameters, and next steps.

Review the conditional gan implementation with label conditioning, one-hot encoding, and a 200-noise, 1000-context projection, sigmoid output, and a scheduler-driven training loop.

Review how DCGAN uses convolutional neural networks to stabilize unsupervised learning, with learned downsampling via stride, no pooling or fully connected layers, batch normalization, and latent space vector arithmetic.

Implement the generator for a dcgan from scratch, using a 100-dimensional latent input and conv transpose 2d upsampling to 64×64 with batch norm and ReLU.

Implement a from-scratch GAN discriminator using a CNN that maps 3 channels to 64, 128, 256, and 512 with padding, stride, leaky ReLU, and batch normalization, ending with a sigmoid.

Create a PyTorch dataset from images in a directory, converting to RGB tensors, resizing to 64 by 64, and applying transforms to normalize -1 to 1 for gan data loader.

Explore dataset setup for mastering GAN models: download the bedroom dataset via GitHub, resolve broken links via academia torrent or Kaggle, and prepare LMDB data for training and validation.

Develop a PyTorch data pipeline from scratch by loading WebP images with glob, creating a dataset, and configuring a DataLoader with batch size and shuffle for GAN training.

Apply a custom weight initialization routine in PyTorch, initializing conv and conv transpose weights from a zero mean distribution with std 0.02, and bias to zero, for generator and discriminator.

Integrate data loader into the training loop, move inputs to device, and track progress with tqdm; switch to Adam, test with validation, and dynamically compute batch size across five epochs.

Implement from scratch by configuring the generator and discriminator, initializing weights, and training with Adam (lr 0.0002, beta1 0.5) using a modified data loader to analyze one-epoch results.

Demonstrates training a DCGAN for one epoch, optimizing data loading with eight workers to maximize GPU utilization, generating 25 images of 64×64, and saving the generator at epoch end.

After five epochs, the model renders bedroom scenes and three-dimensional geometry, notes low image resolution from dpi, and previews next session on improved gan training techniques.

Explore the final code walk-through for a GAN, detailing generator and discriminator architecture, hyperparameters, and weight initialization. Learn data loading, transforms, and 64 by 64 image handling for training.

Review cutting-edge GAN training techniques; this lecture covers feature matching, mini-batch discrimination, historical averaging, one-sided label smoothing, and virtual batch normalization, with a focus on semi-supervised learning and image quality.

Compare semi-supervised GANs: feature matching yields poorer visuals, while mini batch discrimination produces high-quality samples; train a classifier with labeled data and a fake class using GAN loss.

implement and test a generator from scratch for an improved gan, building a three-layer mlp with latent size 100, softplus, batch normalization, and a sigmoid output, exploring weight normalization.

Build a five-layer discriminator in PyTorch with weight normalization and ReLU, optionally using Gaussian noise during training, outputting log probabilities for MNIST's ten classes and features for feature matching.

Learn how to implement a GAN training loop from scratch, including supervised and unsupervised data loaders, discriminator and generator training steps, and logging testing accuracy.

Implement the unsupervised loss by reparameterizing real and fake probabilities from logits, use k outputs, and compute the loss on unsupervised and fake data with logsumexp for numerical stability.

Master the logsumexp trick to stabilize the log of a sum of exponentials in machine learning. Rewrite as max plus log of shifted exponentials to improve numerical stability in PyTorch.

Implement the unsupervised loss for a gan, using logsumexp and softplus tricks on z(x) and d(x) for numerical stability, then train the generator with feature matching mse.

Finish the training loop by sampling unsupervised data, generating fake data with the generator, and computing a mean squared error loss between real and fake features.

Switch the discriminator to testing mode to disable the gaussian noise layer, compute pre-softmax probabilities for MNIST testing data, then revert to training mode and evaluate testing accuracy.

Implement helpers to generate MNIST training data with uniform noise and prepare supervised and unsupervised inputs and targets. Normalize data, sample per class examples, and outline loader setup for training.

Update PyTorch code for newer versions by replacing next with next_iter and reshaping inputs to a 1d 784 vector for mlps, with proper device handling for unsupervised data.

Tune batch size and shuffle data to improve GAN training; compare supervised and semi-supervised results with 100 labeled examples and unlabeled data, achieving near 90% testing accuracy and noting overfitting.

Watch a final code walkthrough of a semi-supervised GAN on MNIST, showing about 90% testing accuracy with 100 labeled examples, the Gaussian noise layer, and the logsumexp technique.

Review a 2017 paper that introduces least squares GAN to boost quality and stability, replaces sigmoid cross-entropy, and explains three-label coding and Pearson chi-square divergence.

Implement a generator for a 2d gaussian toy with a 256-d input, 128-d hidden, and 2-d output using tanh; set up discriminator and training loop, replacing the vanilla gan loss.

Implement discriminator from scratch by duplicating the generator with input 2, hidden 128, output 1, and train using mse loss with an unconstrained output, no sigmoid, replacing z with x.

Implement a vanilla gan training loop by updating the discriminator with real and fake data using mse loss, and train the generator to real labels, saving every 5000 steps.

Extend helpers to sample from an eight‑mode Gaussian mixture with a 56‑dimensional latent space, implement batch sampling with a multivariate normal distribution, and prepare kernel estimation plots.

Explore how to retrieve official code from papers with code, adapt plots using matplotlib and seaborn, implement kernel density estimation plots, and troubleshoot KDE plotting across library versions.

Implement a helper to plot a bivariate distribution from scratch using kernel estimation and matplotlib, setting background color, axis limits, and removing ticks. Define plot_distribution to streamline training prep.

Integrate base code and plot images while citing inspirations from other repositories. Configure cuda, build generator and discriminator, apply Adam with separate learning rates, and save checkpoints for results analysis.

Explore implementing GANs from scratch and visualize generator outputs across epochs with KDE plots, refining plots, creating subplots grids, and addressing axis labeling and data resampling challenges.

This scratch implementation fixes the KDE axis with ax, shows model learning across epochs up to 40k, notes mode recovery, and previews a final code walkthrough and image-to-image translation.

Walks through implementing the Alaskan paper's GAN with a 256-dim latent size and 2d output, using no discriminator sigmoid, mean squared error loss, and multivariate normal data sampling for visualization.

Explore the Pixtopix paper's general-purpose conditional gan for image-to-image translation, emphasizing a task-agnostic, data-driven approach using L1 and gan losses, patch gan, and skip connections.

Implement a pix2pix style generator from scratch using a U‑Net with an encoder and decoder, skip connections, and a 3‑channel RGB input mapped to a 3‑channel output via a tanh.

Implement a generator from scratch by building downsampling convolution blocks and upsampling conv transpose blocks with batch normalization, leaky relu and dropout, forming a u-net style architecture tested on gpu.

Implement a PatchGAN discriminator that operates on 70 by 70 patches, mirrors an encoder with downsampling, uses leaky relu and batch normalization, and outputs a sigmoid probability for GAN training.

Learn to implement patch-based GANs from scratch with PatchGAN, handle per-patch probabilities, and concatenate conditioning with generated images along the channel dimension in the generator and discriminator.

Implement a conditional GAN training loop that alternates discriminator and generator updates, uses data loader and scheduler, and blends GAN and L1 losses with a lambda, using BCE with logits.

Clone the Pixtopix repo and download the facade dataset to prepare the data loader; inspect images and learn to split a single image containing two panels into inputs for training.

Initialize neural network weights with a random Gaussian distribution (mean zero, std 0.02) and set batch norm 2d to mean one, same std, and zero bias, then proceed to training.

Implement a from-scratch GAN training loop with generator and discriminator, init weights, PyTorch optimizers, 200 epochs, and a linear learning-rate decay after epoch 100.

Remove batch normalization in the down convolution block with 512 input and output channels and debug PyTorch code, training over 200 epochs on 400 images to study a function approximator.

Analyze training results by plotting input, ground truth, and generated output in four rows and three columns, using a test data loader and a generator, and compare with wgan.

Walks through the final code for a pix2pix-style GAN with a U-Net generator and 70x70 PatchGAN discriminator, trained on a 400-image facades dataset using GAN and L1 losses.

Examine the Wgan paper’s stable loss using the wasserstein distance, implement Lipschitz constraints, and compare its training stability and informative loss curves against vanilla gan and mode collapse.

Implement a DCGAN-style generator from scratch, mirroring the architecture with convolution transpose 2D blocks, upscaling from 100-noise to 64×64 images, using batch normalization, ReLU, and tanh.

Implement the discriminator with DCGAN-style blocks, remove sigmoid for an unconstrained f(x), test its single-value output on g(z), and move both networks to the GPU to focus on training.

Implement a GAN training loop with a generator and a discriminator (critic), clip weights, and train the discriminator more times per generator update using n critic.

Learn how training the critic approximates the Wasserstein distance by finding f to compute w via the supremum.

Implement a GAN from scratch by wiring the generator and critic, applying rmsprop with a 5e-5 learning rate, 64 batch size, Gaussian noise, and a modified loss and training loop.

Fixes training bugs, reloads the model in a new environment, clarifies data loader iteration and batch-size handling, and discusses smoothing the critic loss, checkpoints, and gradient-penalty plans for WGAN.

Walk through the WGAN implementation, showing Wasserstein estimates and early stopping to reduce overfitting, plus a DCGAN-style generator and critic with weight clipping.

Examine the evolution of Wasserstein GAN training and compare weight clipping with gradient penalty. Learn how gradient-norm regularization yields stable, high-quality image and text generation.

Duplicate wgan code, apply gradient penalty with a regularization term to constrain the critic norm, remove weight clipping, and project noise to a 2d space before reshaping into an image.

Implement a generator with an up ResNet block using a shortcut and pixel shuffle to trade channels for spatial resolution, followed by two conv blocks with batch norm and ReLU.

Implement the discriminator as a mirror of the generator, using 3-channel inputs, down ResNet blocks, progressive channel growth to 512, and a final linear single-value output.

Implement the down ResNet block for the discriminator by mirroring the generator, using average pooling and instance norm 2D, and set up a 128-length latent vector. Test d(g(z)) to validate.

Implement a from-scratch GAN training loop by replacing weight clipping with a lambda gradient penalty, computing the penalty from interpolated real and generated data and backpropagating through the critic.

Adjust from-scratch training by using Adam with default betas, remove special weight initialization, set critic updates to five, apply gradient penalty ten, and train for 25,000 epochs before analyzing results.

Observe training results, noting the correlation between the training curve, critic loss, sample quality, and how cyclegan enables unpaired image translation.

Review the final code walk-through of a wgan with gradient penalty. See the moving average smoothed training loss, 1d latent input, and ResNet generator with 25k iterations delivering sample quality.

Review of a bidirectional GAN with an inference network that maps x to z and back, training on joint distributions to achieve coherent generation and accurate reconstructions.

Implement a two-generator dcgan inference network: encoder-like generator z predicts a Gaussian mu and log sigma from x, samples z via reparameterization, then feeds generator x.

Implement the generator that maps z to x in a standard generative adversarial network, using a sigmoid output to bound pixels between 0 and 1 and no noise.

Design a two-branch discriminator that processes x and z, concatenates features, and outputs a probability. Train it with real and generated pairs to enable bidirectional mapping between z and x.

Implement a dual-generator GAN training loop from scratch with two optimizers, a shared discriminator, SVHN data, and binary cross-entropy losses for iterative learning.

Load the svhn data with scipy.io.loadmat, examine train X, and convert it to a PyTorch tensor, normalize by 255, and prepare a sample minibatch function for GAN training.

Implement the init read function and main loop for a two-generator GAN, using PyTorch weight initialization and two optimizers, while debugging device alignment and adding training progress tracking.

Training completes with a bug fix from generator z to generator x, then presents DCGAN results and notes future encoding, decoding, and adversarial learned inference work.

Explains loading and preprocessing the SVHN street view digits dataset, building two generators (x and z) and a two-head discriminator for a bidirectional gan, with q(z|x) gaussian and reparameterization.