Stars
Code for our NeurIPS 2022 paper
A curated list of resources on implicit neural representations.
Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.
Code for SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning (ICLR 2021)
An efficient video loader for deep learning with smart shuffling that's super easy to digest
(ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
Implementation of NÜWA, state of the art attention network for text to video synthesis, in Pytorch
Large-scale text-video dataset. 10 million captioned short videos.
This repository focus on Image Captioning & Video Captioning & Seq-to-Seq Learning & NLP
An implementation of the paper "Learning to Reweight Examples for Robust Deep Learning" from ICML 2018 with PyTorch and Higher.
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
[NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Code for the paper "Adversarial Self-supervised Contrastive Learning" (NeurIPS 2020)
😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
Code for Unsupervised Image Captioning
PRML algorithms implemented in Python
Command-line program to download videos from YouTube.com and other video sites
A curated list of deep learning resources for video-text retrieval.
Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。
A curated list of research papers in Video Captioning
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning