Class-incremental Novel Class Discovery (ECCV2022)

Subhankar Roy†, Mingxuan Liu†, Zhun Zhong, Nicu Sebe, and Elisa Ricci

† equal contribution

This Github repository presents the PyTorch implementation for the paper Class-incremental Novel Class Discovery [arXiv], accepted with a poster presentation at European Conference on Computer Vision (ECCV) held at Tel Aviv International Convention Center on October 23-27, 2022.

Python >= 3.8.8
PyTorch >= 1.10.0 

environment.yaml includes all the dependencies for conda installation. To install (Please pre-install Anaconda):

conda env create -f environment.yaml

To activate the installed environment:

conda activate iNCD

Option 1

• Download our prepared datasets (CIFAR-10, CIFAR-100 and TinyImagenet) from drive datasets
• Move the downloaded datasets.zip file to ./data/ folder.

# cd to the repository root
cd data
unzip datasets.zip

Option 2

• For CIFAR-10 and CIFAR-100 simply download the datasets and put into ./data/datasets/.
• For TinyImagenet, to download and generate image folders to ./data/datasets/. Please follow https://github.com/tjmoon0104/pytorch-tiny-imagenet

# Pre-train on CIFAR-10 (# of base classes: 5)
CUDA_VISIBLE_DEVICES=0 sh step1_scripts/pretrain_cifar10.sh

# Pre-train on CIFAR-100 (# of base classes: 80)
CUDA_VISIBLE_DEVICES=0 sh step1_scripts/pretrain_cifar100.sh

# Pre-train on TinyImagenet (# of base classes: 180)
CUDA_VISIBLE_DEVICES=0 sh step1_scripts/pretrain_tinyimagenet.sh

# class-iNCD on CIFAR-10 (# of novel classes: 5)
CUDA_VISIBLE_DEVICES=0 sh step2_scripts_cifar10/incd_OG_FRoST.sh

# class-iNCD on CIFAR-100 (# of novel classes: 20)
CUDA_VISIBLE_DEVICES=0 sh step2_scripts_cifar100/incd_OG_FRoST.sh

# class-iNCD on TinyImagenet (# of novel classes: 20)
CUDA_VISIBLE_DEVICES=0 sh step2_scripts_tinyimagenet/incd_OG_FRoST.sh

# Two-step class-iNCD on CIFAR-100 (80-10-10)
CUDA_VISIBLE_DEVICES=0 sh two-steps_scripts/auto_2step_incd_OG_FRoST_cifar100.sh

# Two-step class-iNCD on TinyImagenet (180-10-10)
CUDA_VISIBLE_DEVICES=0 sh two-steps_scripts/auto_2step_incd_OG_FRoST_tinyimagenet.sh

You can use the following scripts to test the trained models under class-iNCD and two-step class-iNCD settings.

We also provide our trained models which you can use to reproduce the experimental results in our paper:

• Download our trained model weights from drive trained models
• Move the downloaded experiments.zip file to ./data/ folder. Then:

# cd to the repository root
cd data
unzip experiments.zip # Note: this will replace your saved model weights in your `./data/experiments/` folder

# CIFAR-10
sh test_cifar10/test_FRoST_incd.sh

# CIFAR-100
sh test_cifar100/test_FRoST_incd.sh

# TinyImagenet
sh test_tinyimagenet/test_FRoST_incd.sh

# Two-step class-iNCD on CIFAR-100 (80-10-10)
sh test_cifar100/test_FRoST_2step_incd.sh

# Two-step class-iNCD on TinyImagenet (180-10-10)
sh test_tinyimagenet/test_FRoST_2step_incd.sh

Table 1: Comparison with state-of-the-art methods in class-iNCD

Table 2: Comparison with the state-of-the-art methods in the two-step class-iNCD setting where new classes arrive in two episodes, instead of one. New-1-J: new classes performance from joint head at first step, New-1-N: new classes performance from novel head at first step, etc

Table 3: Ablation study on the proposed feature distillation (FD), feature replay (FR) and self-training (ST) that form our FRoST

Table 4: Ablation study comparing FRoST with LwF (logits-KD)

Table 5: Ablation study on having a single and separated heads for old and new classes. Joint: class-agnostic head; Novel: new classes classifier head

@inproceedings{roy2022class,
  title={Class-incremental Novel Class Discovery},
  author={Roy, Subhankar and Liu, Mingxuan and Zhong, Zhun and Sebe, Nicu and Ricci, Elisa},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2022}}