Neighborhood Contrastive Learning for Novel Class Discovery

Related tags

Deep LearningNCL
Overview

Neighborhood Contrastive Learning for Novel Class Discovery

License PyTorch

This repository contains the official implementation of our paper:

Neighborhood Contrastive Learning for Novel Class Discovery, CVPR 2021
Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, Nicu Sebe

Requirements

PyTorch >= 1.1

Data preparation

We follow AutoNovel to prepare the data

By default, we save the dataset in ./data/datasets/ and trained models in ./data/experiments/.

  • For CIFAR-10 and CIFAR-100, the datasets can be automatically downloaded by PyTorch.

  • For ImageNet, we use the exact split files used in the experiments following existing work. To download the split files, run the command: sh scripts/download_imagenet_splits.sh . The ImageNet dataset folder is organized in the following way:

    ImageNet/imagenet_rand118 #downloaded by the above command
    ImageNet/images/train #standard ImageNet training split
    ImageNet/images/val #standard ImageNet validation split
    

Pretrained models

We use the pretrained models (self-supervised learning and supervised learning) provided by AutoNovel. To download, run:

sh scripts/download_pretrained_models.sh

If you would like to train the self-supervised learning and supervised learning models by yourself, please refer to AutoNovel for more details.

After downloading, you can go to perform our neighbor contrastive learning below.

Neighborhood Contrastive Learning for Novel Class Discovery

CIFAR10/CIFAR100

Without Hard Negative Generation (w/o HNG)
# Train on CIFAR10
CUDA_VISIBLE_DEVICES=0 sh scripts/ncl_cifar10.sh ./data/datasets/CIFAR/ ./data/experiments/ ./data/experiments/pretrained/supervised_learning/resnet_rotnet_cifar10.pth

# Train on CIFAR100
CUDA_VISIBLE_DEVICES=0 sh scripts/ncl_cifar100.sh ./data/datasets/CIFAR/ ./data/experiments/ ./data/experiments/pretrained/supervised_learning/resnet_rotnet_cifar100.pth
With Hard Negative Generation (w/ HNG)
# Train on CIFAR10
CUDA_VISIBLE_DEVICES=0 sh scripts/ncl_hng_cifar10.sh ./data/datasets/CIFAR/ ./data/experiments/ ./data/experiments/pretrained/supervised_learning/resnet_rotnet_cifar10.pth

# Train on CIFAR100
CUDA_VISIBLE_DEVICES=0 sh scripts/ncl_hng_cifar100.sh ./data/datasets/CIFAR/ ./data/experiments/ ./data/experiments/pretrained/supervised_learning/resnet_rotnet_cifar100.pth

Note that, for cifar-10, we suggest to train the model w/o HNG, because the results of w HNG and w/o HNG for cifar-10 are similar. In addition, the model w/ HNG sometimes will collapse, but you can try different seeds to get the normal result.

ImageNet

Without Hard Negative Generation (w/o HNG)
# Subset A
CUDA_VISIBLE_DEVICES=0 python ncl_imagenet.py --unlabeled_subset A --model_name resnet_imagenet_ncl

# Subset B
CUDA_VISIBLE_DEVICES=0 python ncl_imagenet.py --unlabeled_subset B --model_name resnet_imagenet_ncl

# Subset C
CUDA_VISIBLE_DEVICES=0 python ncl_imagenet.py --unlabeled_subset C --model_name resnet_imagenet_ncl
With Hard Negative Generation (w/o HNG)
# Subset A
CUDA_VISIBLE_DEVICES=0 python ncl_imagenet.py --hard_negative_start 3 --unlabeled_subset A --model_name resnet_imagenet_ncl_hng

# Subset B
CUDA_VISIBLE_DEVICES=0 python ncl_imagenet.py --hard_negative_start 3 --unlabeled_subset B --model_name resnet_imagenet_ncl_hng

# Subset C
CUDA_VISIBLE_DEVICES=0 python ncl_imagenet.py --hard_negative_start 3 --unlabeled_subset C --model_name resnet_imagenet_ncl_hng

Acknowledgement

Our code is heavily designed based on AutoNovel. If you use this code, please also acknowledge their paper.

Citation

We hope you find our work useful. If you would like to acknowledge it in your project, please use the following citation:

@InProceedings{Zhong_2021_CVPR,
      author    = {Zhong, Zhun and Fini, Enrico and Roy, Subhankar and Luo, Zhiming and Ricci, Elisa and Sebe, Nicu},
      title     = {Neighborhood Contrastive Learning for Novel Class Discovery},
      booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
      month     = {June},
      year      = {2021},
      pages     = {10867-10875}
}

Contact me

If you have any questions about this code, please do not hesitate to contact me.

Zhun Zhong

Owner
Zhun Zhong
Zhun Zhong
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Code in both PyTorch and TensorFlow

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context This repository contains the code in both PyTorch and TensorFlow for our paper

Zhilin Yang 3.3k Jan 06, 2023
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Graph ConvNets in PyTorch October 15, 2017 Xavier Bresson http://www.ntu.edu.sg/home/xbresson https://github.com/xbresson https://twitter.com/xbresson

Xavier Bresson 287 Jan 04, 2023
Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022)

Blockwise Sequential Model Learning Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022) For ins

2 Jun 17, 2022
Implementation of the state-of-the-art vision transformers with tensorflow

ViT Tensorflow This repository contains the tensorflow implementation of the state-of-the-art vision transformers (a category of computer vision model

Mohammadmahdi NouriBorji 2 Mar 16, 2022
Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021, Pytorch)

S2VD Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021) Requirements and Dependencies Ubuntu 16.04, cuda 10.0 Python 3.6.10, P

Zongsheng Yue 53 Nov 23, 2022
This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

TransFG: A Transformer Architecture for Fine-grained Recognition Official PyTorch code for the paper: TransFG: A Transformer Architecture for Fine-gra

Ju He 307 Jan 03, 2023
Implementation of 'X-Linear Attention Networks for Image Captioning' [CVPR 2020]

Introduction This repository is for X-Linear Attention Networks for Image Captioning (CVPR 2020). The original paper can be found here. Please cite wi

JDAI-CV 240 Dec 17, 2022
This code is part of the reproducibility package for the SANER 2022 paper "Generating Clarifying Questions for Query Refinement in Source Code Search".

Clarifying Questions for Query Refinement in Source Code Search This code is part of the reproducibility package for the SANER 2022 paper "Generating

Zachary Eberhart 0 Dec 04, 2021
Code for Temporally Abstract Partial Models

Code for Temporally Abstract Partial Models Accompanies the code for the experimental section of the paper: Temporally Abstract Partial Models, Khetar

DeepMind 19 Jul 13, 2022
DziriBERT: a Pre-trained Language Model for the Algerian Dialect

DziriBERT DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian

117 Jan 07, 2023
Release of the ConditionalQA dataset

ConditionalQA Datasets accompanying the paper ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers. Disclaimer This dataset

14 Oct 17, 2022
A Fast Sequence Transducer Implementation with PyTorch Bindings

transducer A Fast Sequence Transducer Implementation with PyTorch Bindings. The corresponding publication is Sequence Transduction with Recurrent Neur

Awni Hannun 184 Dec 18, 2022
Classification of EEG data using Deep Learning

Graduation-Project Classification of EEG data using Deep Learning Epilepsy is the most common neurological disease in the world. Epilepsy occurs as a

Osman Alpaydın 5 Jun 24, 2022
Official DGL implementation of "Rethinking High-order Graph Convolutional Networks"

SE Aggregation This is the implementation for Rethinking High-order Graph Convolutional Networks. Here we show the codes for citation networks as an e

Tianqi Zhang (张天启) 32 Jul 19, 2022
A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Segnet is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This is implementation of http://arxiv.org/pdf/15

Pradyumna Reddy Chinthala 190 Dec 15, 2022
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation Official PyTorch Implementation

: We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity, the w

Yuval Nirkin 182 Dec 14, 2022
TigerLily: Finding drug interactions in silico with the Graph.

Drug Interaction Prediction with Tigerlily Documentation | Example Notebook | Youtube Video | Project Report Tigerlily is a TigerGraph based system de

Benedek Rozemberczki 91 Dec 30, 2022
Implementation of ProteinBERT in Pytorch

ProteinBERT - Pytorch (wip) Implementation of ProteinBERT in Pytorch. Original Repository Install $ pip install protein-bert-pytorch Usage import torc

Phil Wang 92 Dec 25, 2022
BoxInst: High-Performance Instance Segmentation with Box Annotations

Introduction This repository is the code that needs to be submitted for OpenMMLab Algorithm Ecological Challenge, the paper is BoxInst: High-Performan

88 Dec 21, 2022
Source code for our paper "Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash"

Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash Abstract: Apple recently revealed its deep perceptual hashing system NeuralHash to

<a href=[email protected]"> 11 Dec 03, 2022