jason718 / awesome-self-supervised-learning Goto Github PK

Many thanks for your kind sharing! Would you please help us to share our CVPR 2024 paper about SSL? Thank you very much!

Paper title: VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis
Publication: CVPR 2024
Paper link: https://arxiv.org/abs/2402.17300
Code link: https://github.com/Luffy03/VoCo

https://github.com/Philip-Bachman/amdim-public

@jason718

This is a really helpful list for the self-supervised learning.

The survey paper 'Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey.' is accepted by TPAMI 2020. Can you help to update it? Thanks!

The IEEE link is: https://ieeexplore.ieee.org/abstract/document/9086055

Hi @jason718, could you maybe add this paper, which uses self-supervised learning for image-based localization? Thanks!

• Geometry-Aware Learning of Maps for Camera Localization.
  [pdf]
  [code]
• Samarth Brahmbhatt, Jinwei Gu, Kihwan Kim, James Hays, and Jan Kautz. CVPR 2018

Cool list! May I suggest adding the following paper to the Timeseries section:

Deldari, Shohreh, Dimitris Spathis, Mohammad Malekzadeh, Fahim Kawsar, Flora D. Salim, and Akhil Mathur. "CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking." In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp. 152-160. 2024. https://arxiv.org/abs/2307.16847

should be:
https://arxiv.org/abs/1904.09117

thanks for effort :)

It's a nice repo! Just wondering why you didn't include the work from the Yann LeCun's group (did you)? I thought the name "self-supervised-learning" came from his group...maybe I was wrong since I am new to self-supervised-learning...:)

The Sound of Pixels.
https://eccv2018.org/openaccess/content_ECCV_2018/papers/Hang_Zhao_The_Sound_of_ECCV_2018_paper.pdf (Official Link), https://arxiv.org/pdf/1907.11879.pdf (PDF)
https://github.com/hangzhaomit/Sound-of-Pixels (CODE)
Zhao, Hang and Gan, Chuang and Rouditchenko, Andrew and Vondrick, Carl and McDermott, Josh and Torralba, Antonio.

help needed! please consider contributing.

Multi-task Self-Supervised Learning for Human Activity Detection.
https://dl.acm.org/citation.cfm?id=3328932 (Official Link), https://arxiv.org/pdf/1907.11879.pdf (PDF)
Aaqib Saeed, Tanir Ozcelebi, Johan Lukkien. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.

Could you maybe add this paper, which uses self-supervised learning for Recommendation? Thanks!

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
pdf
code
Lei, Chen and Le, Wu and Kun, Zhang and Richang, Hong and Meng, Wang. SIGIR 2021

Great collection. Two papers missed probably:

Joint Unsupervised Learning of Deep Representations and Image Clusters. CVPR 2016 (yes, I am one of the authors, :))

Unsupervised Deep Embedding for Clustering Analysis. ICML 2016

The PDF link of ''Representation Learning by Learning to Count'' is https://arxiv.org/abs/1708.06734. Please correct it.

Hi, thanks for this great list of works, it's been very useful ! Would you be able to add links to codes for the following two papers please :) ?

• DeepCluster (Deep Clustering for Unsupervised Learning of Visual Features): https://github.com/facebookresearch/deepcluster
• SwAV (Unsupervised Learning of Visual Features by Contrasting Cluster Assignments): https://github.com/facebookresearch/swav

Hi,

Thanks for maintaining this repository. Appreciate your efforts.

Please add our paper "Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation" that provides a novel way
to learn powerful representations in Semi-Supervised Setting using Contrastive Learning for images.
arXiv: https://arxiv.org/abs/2106.06801

The paper is accepted at MICCAI 2021.

We appreciate your effort on the maintenance of this awesome repository.
We would like to recommend our recent research works on contrastive learning, which achieve state-of-the-art performance on popular benchmarks.
The papers are listed as follows:
Inter-Instance Similarity Modeling for Contrastive Learning
Asymmetric Patch Sampling for Contrastive Learning
Both of them provides open source code and trained model weights at Github, which can be found at following links:
https://github.com/visresearch/patchmix
https://github.com/visresearch/aps

Thanks!

Hi @jason718 . Could you maybe add this review paper on future video frame prediction? Many thanks!

• A Review on Deep Learning Techniques for Video Prediction (arxiv, pami)
• Sergiu Oprea, Pablo Martinez-Gonzalez, Alberto Garcia-Garcia, John Alejandro Castro-Vargas, Sergio Orts-Escolano, Jose Garcia-Rodriguez, and Antonis Argyros.
• T-PAMI (2020)