[NeurIPS 2021] The PyTorch implementation of paper "Self-Supervised Learning Disentangled Group Representation as Feature"

Last update: Dec 24, 2022

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[NeurIPS 2021] The PyTorch implementation of paper "Self-Supervised Learning Disentangled Group Representation as Feature". Codes will be released recently.

If you find our codes helpful, please cite our paper:

@inproceedings{wang2021self,
  title={Self-Supervised Learning Disentangled Group Representation as Feature},
  author={Wang, Tan and Yue, Zhongqi and Huang, Jianqiang and Sun, Qianru and Zhang, Hanwang},
  booktitle={Conference and Workshop on Neural Information Processing Systems (NeurIPS)},
  year={2021}
}

Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

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Code for our NeurIPS 2021 paper Mining the Benefits of Two-stage and One-stage HOI Detection

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Companion code for the paper "An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence" (NeurIPS 2021)

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Comments

Coufused about the t-SNE result of CMNIST

Hi~ Thanks for your exciting work! But I am a little confused about the t-SNE result of CMNIST(Figure 4(a)). Why do there exist two parts ?（bottom left and top right）

opened by wangkiw 2
Pretrained weights

Are there any pretrained model weights available (the CIFAR100 ones for example)? I want to evaluate the features against several other SSL algorithms I have.

opened by ShairozS 1
Reproducing CIFAR100 results

Hi. Thank you for sharing your work. I wanted to reproduce your CIFAR100 results. However, my training is broken in the middle. I attached the screenshot here. Also, I would like to request the ImageNet training script. When I tried to adapt your code for ImageNet data, I'm facing Cuda memory issue. Also, I wonder about the negative loss in Updating Env.!! Thanks, Raj a

opened by PushparajaMurugan 0

Owner

Wang Tan

Ph.D. student of MreaL Lab, NTU

GitHub

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