March 24, 2022 Self-Supervised

Watermarking Images in Self-Supervised Latent-Spaces

PyTorch implementation and pretrained models for the paper. For details, see Watermarking Images in Self-Supervised Latent-Spaces. If you find this repository useful, please consider giving a star ⭐ and please cite as: @inproceedings{fernandez2022sslwatermarking, title={Watermarking Images in Self-Supervised Latent Spaces}, author={Fernandez, Pierre and Sablayrolles, Alexandre and Furon, Teddy and Jégou, Hervé and Douze, Matthijs}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2022}, organization={IEEE}, } [Webpage] [arXiv] [Spaces] [Colab] Introduction To finish reading, please visit source site

January 9, 2022 Self-Supervised

A self-supervised learning framework for audio-visual speech

Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction Robust Self-Supervised Audio-Visual Speech Recognition Introduction AV-HuBERT is a self-supervised representation learning framework for audio-visual speech. It achieves state-of-the-art results in lip reading, ASR and audio-visual speech recognition on the LRS3 audio-visual speech benchmark. If you find AV-HuBERT useful in your research, please use the following BibTeX entry for citation.

October 23, 2021 Self-Supervised

Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models, NeurIPS 2021). Setup Create the conda environment by running : conda env create -f environment.yml Alternatively, you can update an existing conda environment by running : conda env update -f environment.yml Modify the python pathexport PYTHONPATH= Export the following variables for renderingexport MUJOCO_RENDERER=egl; export MUJOCO_GL=egl Please follow these instructions to install mujoco Bibtex If you find this code useful, please cite:

October 22, 2021 Self-Supervised

Self-Supervised Learning by Estimating Twin Class Distribution

Codes and pretrained models for TWIST: @article{wang2021self, title={Self-Supervised Learning by Estimating Twin Class Distributions}, author={Wang, Feng and Kong, Tao and Zhang, Rufeng and Liu, Huaping and Li, Hang}, journal={arXiv preprint arXiv:2110.07402}, year={2021} } TWIST is a novel self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a softmax operation to produce twin class distributions of two augmented images. Without supervision, we enforce the class distributions of different augmentations to […]

August 19, 2021 Self-Supervised

A Unified Framework for Self-Supervised Outlier Detection

SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021] Pdf: https://openreview.net/forum?id=v5gjXpmR8J Code for our ICLR 2021 paper on outlier detection, titled SSD, without requiring class labels of in-distribution training data. We leverage recent advances in self-supervised representation learning followed by the cluster-based outlier detection to achieve competitive performance. This repository support both self-supervised training of networks and outlier detection evaluation of pre-trained networks. It also includes code for the two proposed extensions in the paper, i.e., 1) Few-shot outlier […]

August 12, 2021 Self-Supervised

Self-Supervised Learning with Vision Transformers

Self-Supervised Learning with Vision Transformers By Zhenda Xie*, Yutong Lin*, Zhuliang Yao, Zheng Zhang, Qi Dai, Yue Cao and Han Hu This repo is the official implementation of “Self-Supervised Learning with Swin Transformers”. A important feature of this codebase is to include Swin Transformer as one of the backbones, such that we can evaluate the transferring performance of the learnt representations on down-stream tasks of object detection and semantic segmentation. This evaluation is usually not included in previous works due […]