Essentials for Class Incremental Learning

Official repository of the paper 'Essentials for Class Incremental Learning'

This Pytorch repository contains the code for our work Essentials for Class Incremental Learning.

This work presents a straightforward class-incrmental learning system that focuses on the essential components and already exceeds the state of the art without integrating sophisticated modules.

Requirements

To install requirements:

pip install -r requirements.txt

Training and Evaluation (CIFAR-100, ImageNet-100, ImageNet-1k)

Following scripts contain both training and evaluation codes. Model is evaluated after each phase in class-IL.

with Knowledge-distillation (KD)

To train the base CCIL model:

bash ./scripts/run_cifar.sh
bash ./scripts/run_imagenet100.sh
bash ./scripts/run_imagenet1k.sh

To train CCIL + Self-distillation

bash ./scripts/run_cifar_w_sd.sh
bash ./scripts/run_imagenet100_w_sd.sh
bash ./scripts/run_imagenet1k_w_sd.sh

Results (CIFAR-100)

Model name	Avg Acc (5 iTasks)	Avg Acc (10 iTasks)
CCIL	66.44	64.86
CCIL + SD	67.17	65.86

Results (ImageNet-100)

Model name	Avg Acc (5 iTasks)	Avg Acc (10 iTasks)
CCIL	77.99	75.99
CCIL + SD	79.44	76.77

Results (ImageNet)

Model name	Avg Acc (5 iTasks)	Avg Acc (10 iTasks)
CCIL	67.53	65.61
CCIL + SD	68.04	66.25

List of Arguments

Distillation Methods
- Knowledge Distillation (--kd, --w-kd X), X is the weightage for KD loss, default=1.0
- Representation Distillation (--rd, --w-rd X), X is the weightage for cos-RD loss, default=0.05
- Contrastive Representation Distillation (--nce, --w-nce X), only valid for CIFAR-100, X is the weightage of NCE loss
Regularization for the first task
- Self-distillation (--num-sd X, --epochs-sd Y), X is number of generations, Y is number of self-distillation epochs
- Mixup (--mixup, --mixup-alpha X), X is mixup alpha value, default=0.1
- Heavy Augmentation (--aug)
- Label Smoothing (--label-smoothing, --smoothing-alpha X), X is a alpha value, default=0.1
Incremental class setting
- No. of base classes (--start-classes 50)
- 5-phases (--new-classes 10)
- 10-phases (--new-classes 5)
Cosine learning rate decay (--cosine)
Save and Load
- Experiment Name (--exp-name X)
- Save checkpoints (--save)
- Resume checkpoints (--resume, --resume-path X), only to resume from first snapshot

Citation

@InProceedings{Mittal_2021_CVPR,
    author    = {Mittal, Sudhanshu and Galesso, Silvio and Brox, Thomas},
    title     = {Essentials for Class Incremental Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2021},
    pages     = {3513-3522}
}

sud0301/essentials_for_CIL