PESCR: Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning

Overview

Class-incremental learning (CIL) with Prompt-Based Exemplar Super-Compression and Regeneration (PESCR), a diffusion-based approach that boosts CIL performance by storing exemplars with increased quantity and enhanced diversity under limited memory budget. PESCR works by

• extracting visual and textual prompts from selected images and saving prompts instead of images
• regenerating exemplars from prompts with ControlNet for CIL model training in subsequent phases

Paper

Official implementation of

Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning [BMVC 2025]
Ruxiao Duan¹, Jieneng Chen², Adam Kortylewski^3,4, Alan Yuille², Yaoyao Liu⁵,
¹Yale University, ²Johns Hopkins University, ³University of Freiburg, ⁴Max Planck Institute for Informatics, ⁵University of Illinois Urbana-Champaign

Getting Started

1. Environment Creation

git clone https://github.com/lllyasviel/ControlNet.git
conda env create -f ControlNet/environment.yaml
conda activate control

2. ControlNet Preparation

• Download ControlNet from HuggingFace and save the Canny edge model (control_sd15_canny.pth) in ControlNet/models/control_sd15_canny.pth.

3. Data Curation

• Choose an image classification dataset and save all images in the form of <data-dir>/<dataset-name>/<mode>/<class-label>/<image-filename>
  • <data-dir>: directory to store all datasets.
  • <dataset-name>: name of the dataset.
  • <mode>: either "train" or "test".
  • <class-label>: the class tag, e.g., "cupcakes".
  • <image-filename>: the image filename, e.g., "0001.jpg".
• If the dataset is not implemented, add it to the scripts.
  • In utils/data.py, set another class by specifying the dataset name and its number of categories.
  • In utils/data_manager.py, add a mapping from the dataset name to its class instance in _get_idata().

4. Path Configuration

• In compress/compress.py and compress/compress_lowres.py, set CNET_DIR to <pwd>/ControlNet.
• In compress/compress.py and compress/compress_lowres.py, set DATA_DIR to <data-dir>.
• In utils/data.py, set self.data_dir to <data-dir>.
• In utils/toolkit.py, set return of output_folder() as path to the output folder.

5. Prompt Extraction and Image Generation

• We compress all the training images into edge maps and generate their copies by ControlNet in advance.

• In compress/compress.py and compress/compress_lowres.py, set SEEDS to seeds of your choice, e.g., range(5).

• If the images are relatively high-resolution, run

python compress/compress.py

• If the images are relatively low-resolution, run

python compress/compress_lowres.py

6. Training Configuration

• In exps/config.json set training parameters. Most configurations follow the convention of PyCIL, except:
  • augmentation_prob: the probability of replacing a real image during one of its generated copies during training.
  • augmentations_per_image: the number of generated copies per image.
  • memory_per_class: memory budget in units per class.
  • real_per_class: number of real exemplars per class.
  • syn_per_class: number of synthetic exemplars per class.
• For instance, if we can set memory_per_class=20, real_per_class=18, and syn_per_class=48.

7. CIL Model Training

python main.py

Acknowledgement

The CIL framework is developed based on PyCIL.