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Compositional Experts (ComEx) for GNCD

Code for the CVPR 2022 paper:

Title: Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery<br> Authors: Muli Yang, Yuehua Zhu, Jiaping Yu, Aming Wu, and Cheng Deng<br> Paper: CVF Open Access

Introduction

Abstract: In response to the explosively-increasing requirement of annotated data, Novel Class Discovery (NCD) has emerged as a promising alternative to automatically recognize unknown classes without any annotation. To this end, a model makes use of a base set to learn basic semantic discriminability that can be transferred to recognize novel classes. Most existing works handle the base and novel sets using separate objectives within a two-stage training paradigm. Despite showing competitive performance on novel classes, they fail to generalize to recognizing samples from both base and novel sets. In this paper, we focus on this generalized setting of NCD (GNCD), and propose to divide and conquer it with two groups of Compositional Experts (ComEx). Each group of experts is designed to characterize the whole dataset in a comprehensive yet complementary fashion. With their union, we can solve GNCD in an efficient end-to-end manner. We further look into the drawback in current NCD methods, and propose to strengthen ComEx with global-to-local and local-to-local regularization. ComEx is evaluated on four popular benchmarks, showing clear superiority towards the goal of GNCD. <br>

Setup

• Environment: Our implementation is a fork of UNO v1, so please carefully follow the preparation of the code environment on their page.
• Datasets: We use CIFAR10/100 and ImageNet datasets in our experiments. The CIFAR datasets can be automatically downloaded to the path specified by --data_dir when passing the --download argument. For ImageNet please follow the instructions on its homepage.
• Checkpoints: Our ComEx follows the two-stage training paradigm (supervised pretraining + novel class discovery). We directly use the model checkpoints provided in UNO after the supervised pretraining. The checkpoints can be accessed via Google Drive. We also provide our pretrained checkpoint used in our CIFAR100-70 (30 base classes, 70 novel classes) experiment, which can be downloaded via OneDrive.
• Logging: To use Wandb for logging, please create an account and specify --entity YOUR_ENTITY and --project YOUR_PROJECT. Normally, it requires the Internet accessibility to proceed; otherwise, you can pass --offline for logging locally, and the generated files can also be manually uploaded to Wandb for further analysis.

Commands

Recall that there is a supervised pretraining stage before the discovery phase. If you would like to train your model from scratch, please run the following lines to get a pretrained checkpoint on base classes. Note that your checkpoint will be located in the checkpoints folder by default.

CUDA_VISIBLE_DEVICES=0 python main_pretrain.py \
 --dataset CIFAR10 \
 --data_dir PATH/TO/DATASET \
 --gpus 1 \
 --precision 16 \
 --max_epochs 200 \
 --num_base_classes 5 \
 --num_novel_classes 5 \
 --comment 5_5

After that, you can start novel class discovery using the pretrained checkpoint (or directly using the above provided checkpoint) by specifying --pretrained with your checkpoint path:

CUDA_VISIBLE_DEVICES=0 python main_discover.py \
 --dataset CIFAR10 \
 --data_dir PATH/TO/DATASET \
 --gpus 1 \
 --precision 16 \
 --max_epochs 200 \
 --num_base_classes 5 \
 --num_novel_classes 5 \
 --queue_size 500 \
 --sharp 0.5 \
 --batch_head \
 --batch_head_multi_novel \
 --batch_head_reg 1.0 \
 --pretrained PATH/TO/CHECKPOINTS/pretrain-resnet18-CIFAR10.cp \
 --comment 5_5

Tips for running on different datasets

• Specify --dataset with CIFAR10, CIFAR100 or ImageNet.
• Specify --data_dir with the path where you store the dataset.
• Make sure --num_base_classes and --num_novel_classes accord with the setting you want.
• Modify --comment so that you can easily find the Wandb logs.

When running discovery:

• Specify --pretrained with the path to the checkpoint you would like to use.

For running on ImageNet, please use the following commands:

CUDA_VISIBLE_DEVICES=0 python main_pretrain.py \
 --dataset ImageNet \
 --data_dir PATH/TO/IMAGENET \
 --gpus 1 \
 --precision 16 \
 --max_epochs 100 \
 --warmup_epochs 5 \
 --num_base_classes 882 \
 --num_novel_classes 30 \
 --comment 882_30

CUDA_VISIBLE_DEVICES=0 python main_discover.py \
 --dataset ImageNet \
 --data_dir PATH/TO/IMAGENET \
 --imagenet_split A \
 --gpus 1 \
 --precision 16  \
 --max_epochs 60 \
 --warmup_epochs 5 \
 --num_base_classes 882 \
 --num_novel_classes 30 \
 --queue_size 500 \
 --sharp 0.5 \
 --pretrained PATH/TO/CHECKPOINTS/pretrain-resnet18-ImageNet.cp \
 --comment 882_30-A

• Specify --imagenet_split with A, B or C.

TODO

Acknowledgements

Our work is inspired from many recent efforts in various fields. They are

• UNO
• SwAV
• PAWS
• CoMatch
• ACE
• TADE
• MSF
• NCL
• SCAN

Many thanks for their great work!

Citations

If you find our work helpful, please consider citing our paper:

@InProceedings{yang2022divide,
    author    = {Yang, Muli and Zhu, Yuehua and Yu, Jiaping and Wu, Aming and Deng, Cheng},
    title     = {Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {14268-14277}
}

If you use our code, please also consider citing the paper of UNO:

@InProceedings{Fini_2021_ICCV,
    author    = {Fini, Enrico and Sangineto, Enver and Lathuili\`ere, St\'ephane and Zhong, Zhun and Nabi, Moin and Ricci, Elisa},
    title     = {A Unified Objective for Novel Class Discovery},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {9284-9292}
}