Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

Tensorflow implementation of variational auto-encoder for MNIST

Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.

No description available

Implementation of VAE and CVAE using Pytorch on MNIST dataset

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

Pytorch Implementation of variational auto-encoder for MNIST

PyTorch implementation of Variational Autoencoder (VAE) on MNIST dataset.

Semi-supervised learning with mnist using variational autoencoders. An unsupervised representation is learned which allows for superior classification results with limited labels.

Semi-Supervised Learning with Categorical VAE (experimented on MNIST)

VQ-VAE implementation in Pytorch

No description available

Conditional variational autoencoder applied to EMNIST + an interactive demo to explore the latent space.

VAE-GAN applied to the MNIST Digits

关于VAE和CVAE生成手写数字图片的实现

Conditional VAE using CNN on MNIST in PyTorch

Pytorch implementation of a Variational Autoencoder (VAE) that learns from the MNIST dataset and generates images of altered handwritten digits.

We present a coupled Variational Auto-Encoder (VAE) method that improves the accuracy and robustness of the probabilistic inferences on represented data. The new method models the dependency between input feature vectors (images) and weighs the outliers with a higher penalty by generalizing the original loss function to the coupled entropy function, using the principles of nonlinear statistical coupling. We evaluate the performance of the coupled VAE model using the MNIST dataset. Compared with the traditional VAE algorithm, the output images generated by the coupled VAE method are clearer and less blurry. The visualization of the input images embedded in 2D latent variable space provides a deeper insight into the structure of new model with coupled loss function: the latent variable has a smaller deviation and the output values are generated by a more compact latent space. We analyze the histograms of probabilities for the input images using the generalized mean metrics, in which increased geometric mean illustrates that the average likelihood of input data is improved. Increases in the -2/3 mean, which is sensitive to outliers, indicates improved robustness. The decisiveness, measured by the arithmetic mean of the likelihoods, is unchanged and -2/3 mean shows that the new model has better robustness.

A minimal example for training a flow matching model in a pretrained VAE's latent space to generate MNIST digits.

No description available

Minimal VAE, Conditional VAE (CVAE), Gaussian Mixture VAE (GMVAE) and Variational RNN (VRNN) in PyTorch, trained on MNIST.

VAE + Quantile Networks for MNIST

Variational autoencoder in Keras on MNIST images

Implement Conditional VAE and train on MNIST by tensorflow 1.3.0.

Dive into the world of Variational Autoencoders (VAEs) with MNIST! 🎨✨ Explore variable latent sizes (2, 4, 16) to see how they affect reconstruction, latent space visualizations, and performance metrics 📊 (MSE, SSIM, PSNR).

VAE for Fashion MNIST with PyTorch

Python code (Keras) to implement a Variational Autoencoder Generative Adversarial Network (Using GAN instead of decoder in VAE). MNIST dataset reconstructed using VAEGAN.

Example of replacing MLP with KAN in autoencoder(AE) and variational autoencoder(VAE)

Implementation of CoordConv (Convolution and Deconvolution) for a Variational Autoencoder applied to MNIST

CodeBook,VQ-VAE,MNIST