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Learn Domain-Driven Design, software architecture, design patterns, best practices. Code examples included
[CVPR 2021 Oral] Official PyTorch implementation of Soft-IntroVAE from the paper "Soft-IntroVAE: Analyzing and Improving Introspective Variational Autoencoders"
A pytorch implementation of the vector quantized variational autoencoder (https://arxiv.org/abs/1711.00937)
Standard federated learning implementations in FedLab and FL benchmarks.
Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet…
RepVGG: Making VGG-style ConvNets Great Again
Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch
Official PyTorch code for the paper "Improving Fractal Pre-training"
Vector Quantized VAEs - PyTorch Implementation
Various Latent Variable Models implementations in Pytorch, including VAE, VAE with AF Prior, VQ-VAE and VQ-VAE with Gated PixelCNN
(ECCV2020 Workshops) Efficient Image Super-Resolution Using Pixel Attention.
Implemented Variational Autoencoder generative model in Keras for image generation and its latent space visualization on MNIST and CIFAR10 datasets
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
A Collection of Variational Autoencoders (VAE) in PyTorch.
pytorch implementation Variational Autoencoder and Conditional Variational Autoencoder
Implementation of VAE and CVAE using Pytorch on MNIST dataset
[ICLR2023] Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning (https://arxiv.org/abs/2210.00226)
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations