Eagle's repositories
CorruptionRobustness
We investigated corruption robustness across different architectures including Convolutional Neural Networks, Vision Transformers, and the MLP-Mixer.
MoCo
A PyTorch implementation of MoCo based on CVPR 2020 paper "Momentum Contrast for Unsupervised Visual Representation Learning"
Patch-Fool
[ICLR 2022] "Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?" by Yonggan Fu, Shunyao Zhang, Shang Wu, Cheng Wan, Yingyan Lin
pytorch-lightning-template
An easy/swift-to-adapt PyTorch-Lighting template. 套壳模板，简单易用，稍改原来Pytorch代码，即可适配Lightning。You can translate your previous Pytorch code much easier using this template, and keep your freedom to edit all the functions as well. Big-project-friendly as well.
Rickrolling-the-Artist
Source code for our paper "Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models".
scenic
Scenic: A Jax Library for Computer Vision Research and Beyond
SimCLR
A PyTorch implementation of SimCLR based on ICML 2020 paper "A Simple Framework for Contrastive Learning of Visual Representations"
SimSiam
A PyTorch implementation of SimSiam based on CVPR 2021 paper "Exploring Simple Siamese Representation Learning"
StreamingCNN
To train deep convolutional neural networks, the input data and the activations need to be kept in memory. Given the limited memory available in current GPUs, this limits the maximum dimensions of the input data. Here we demonstrate a method to train convolutional neural networks while holding only parts of the image in memory.
vision-transformers-cifar10
Let's train vision transformers (ViT) for cifar 10!