AutoNovel

Automatically Discovering and Learning New Visual Categories with Ranking Statistics, ICLR 2020,
Kai Han*, Sylvestre-Alvise Rebuffi*, Sebastien Ehrhardt*, Andrea Vedaldi, Andrew Zisserman

Dependencies

All dependencies are included in environment.yml. To install, run

conda env create -f environment.yml

(Make sure you have installed Anaconda before running.)

Then, activate the installed environment by

conda activate auto_novel

Overview

We provide code and models for our experiments on CIFAR10, CIFAR100, SVHN, OmniGlot, and ImageNet:

Code for self-supervised learning
Code for supervised learning
Code for novel category discovery
Our trained models and all other required pretrained models

Data preparation

By default, we put the datasets in ./data/datasets/ and save trained models in ./data/experiments/ (soft link is suggested). You may also use any other directories you like by setting the --dataset_root argument to /your/data/path/, and the --exp_root argument to /your/experiment/path/ when running all experiments below.

For CIFAR-10, CIFAR-100, and SVHN, simply download the datasets and put into ./data/datasets/.
For OmniGlot, after downloading, you need to put Alphabet_of_the_Magi, Japanese_(katakana), Latin, Cyrillic, Grantha from imags_background folder into images_background_val folder, and put the rest alphabets into images_background_train folder.
For ImageNet, we provide 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 provide our trained models and all other required pretrained models. To download, run:

sh scripts/download_pretrained_models.sh

After downloading, you may directly jump to Step 3 below, if you only want to run our ranking based method.

Step 1: Self-supervised learning with both labelled and unlabelled data

CUDA_VISIBLE_DEVICES=0 python selfsupervised_learning.py --dataset_name cifar10 --model_name rotnet_cifar10 --dataset_root ./data/datasets/CIFAR/

--dataset_name can be one of {cifar10, cifar100, svhn}; --dataset_root is set to ./data/datasets/CIFAR/ for CIFAR10/CIFAR100 and ./data/datasets/SVHN/ for SVHN.

Our code for step 1 is based on the official code of the RotNet paper.

Step 2: Supervised learning with labelled data

# For CIFAR10
CUDA_VISIBLE_DEVICES=0 python supervised_learning.py --dataset_name cifar10 --model_name resnet_rotnet_cifar10

# For CIFAR100
CUDA_VISIBLE_DEVICES=0 python supervised_learning.py --dataset_name cifar100 --model_name resnet_rotnet_cifar100 --num_labeled_classes 80 --num_unlabeled_classes 20

# For SVHN 
CUDA_VISIBLE_DEVICES=0 python supervised_learning.py --dataset_name svhn --model_name resnet_rotnet_svhn --dataset_root ./data/datasets/SVHN/

Step 3: Joint training for novel category discovery

Novel category discovery on CIFAR10/CIFAR100/SVHN

# Train on CIFAR10
CUDA_VISIBLE_DEVICES=0 sh scripts/auto_novel_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/auto_novel_cifar100.sh ./data/datasets/CIFAR/ ./data/experiments/ ./data/experiments/pretrained/supervised_learning/resnet_rotnet_cifar100.pth

# Train on SVHN
CUDA_VISIBLE_DEVICES=0 sh scripts/auto_novel_svhn.sh ./data/datasets/SVHN/ ./data/experiments/ ./data/experiments/pretrained/supervised_learning/resnet_rotnet_svhn.pth

To train in the Incremental Learning (IL) mode, replace auto_novel_{cifar10, cifar100, svhn}.sh in the above commands by auto_novel_IL_{cifar10, cifar100, svhn}.sh.

Novel category discovery on OmniGlot

# For OmniGlot
CUDA_VISIBLE_DEVICES=0 python auto_novel_omniglot.py

Novel category discovery on ImageNet

# For ImageNet subset A
CUDA_VISIBLE_DEVICES=0 python auto_novel_imagenet.py --unlabeled_subset A

# For ImageNet subset B
CUDA_VISIBLE_DEVICES=0 python auto_novel_imagenet.py --unlabeled_subset B

# For ImageNet subset C
CUDA_VISIBLE_DEVICES=0 python auto_novel_imagenet.py --unlabeled_subset C

Evaluation on novel category discovery

To run our code in evaluation mode, set the --mode to test.

# For CIFAR10
CUDA_VISIBLE_DEVICES=0 python auto_novel.py --mode test --dataset_name cifar10 --model_name resnet_cifar10 --exp_root ./data/experiments/pretrained/

# For CIFAR100
CUDA_VISIBLE_DEVICES=0 python auto_novel.py --mode test --dataset_name cifar100 --model_name resnet_cifar100 --exp_root ./data/experiments/pretrained/ --num_labeled_classes 80 --num_unlabeled_classes 20 

# For SVHN
CUDA_VISIBLE_DEVICES=0 python auto_novel.py --mode test --dataset_name svhn --model_name resnet_svhn --exp_root ./data/experiments/pretrained/ --dataset_root ./data/datasets/SVHN

# For OmniGlot
CUDA_VISIBLE_DEVICES=0 python auto_novel_omniglot.py --mode test --model_name vgg6_seed_0 --exp_root ./data/experiments/pretrained/

# For ImageNet subset A
CUDA_VISIBLE_DEVICES=0 python auto_novel_imagenet.py --mode test --unlabeled_subset A --exp_root ./data/experiments/pretrained/

# For ImageNet subset B
CUDA_VISIBLE_DEVICES=0 python auto_novel_imagenet.py --mode test --unlabeled_subset B --exp_root ./data/experiments/pretrained/

# For ImageNet subset C
CUDA_VISIBLE_DEVICES=0 python auto_novel_imagenet.py --mode test --unlabeled_subset C --exp_root ./data/experiments/pretrained/

To perform the evaluation in the Incremental Learning (IL) mode, add in the above commands the argument --IL and replace the model nameresnet_{cifar10, cifar100, svhn} by resnet_IL_{cifar10, cifar100, svhn}.

Citation

If this work is helpful for your research, please cite our paper.

@inproceedings{Han2020automatically,
author    = {Kai Han and Sylvestre-Alvise Rebuffi and Sebastien Ehrhardt and Andrea Vedaldi and Andrew Zisserman},
title     = {Automatically Discovering and Learning New Visual Categories with Ranking Statistics},
booktitle = {International Conference on Learning Representations (ICLR)},
year      = {2020}
}

Acknowledgments

This work is supported by the EPSRC Programme Grant Seebibyte EP/M013774/1, Mathworks/DTA DFR02620, and ERC IDIU-638009.

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