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PiCO: Contrastive Label Disambiguation for Partial Label Learning

This is a PyTorch implementation of ICLR 2022 Oral paper PiCO. Also, see our Project Page.

Title: PiCO: Contrastive Label Disambiguation for Partial Label Learning

Authors: Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao

Affliations: Zhejiang University, University of Wisconsin-Madison, Chongqing University, RIKEN

@inproceedings{
  wang2022pico,
  title={Pi{CO}: Contrastive Label Disambiguation for Partial Label Learning},
  author={Haobo Wang and Ruixuan Xiao and Yixuan Li and Lei Feng and Gang Niu and Gang Chen and Junbo Zhao},
  booktitle={International Conference on Learning Representations},
  year={2022},
  url={https://openreview.net/forum?id=EhYjZy6e1gJ}
}

PiCO+: A Robust Extention to Noisy Partial Labels (TPAMI 2024)

Recently, we released PiCO+, a robust extension that is able to mitigate potentially wrong candidate labels (noisy partial label learning). We refer the readers to README_PiCO_plus.md for more details.

Start Running PiCO

PiCO is easy to be implemented and experiment with. All commands must be ran from the project root.

We provide the following shell codes for model training. Also, see the run.sh file. We didn't carefully tune the best number of epochs. A smaller number of training epochs (e.g. 400~500 epochs with a slightly larger learning rate) can also produce good results, actually.

Data Preparation

For CIFAR datasets, one can directly run the shell codes.

For the CUB200 dataset, we provide a preprocessed copy here and just put the files to data/cub200/processed .

Run cifar10 with q=0.5

CUDA_VISIBLE_DEVICES=0 python -u train.py \
  --exp-dir experiment/PiCO-CIFAR-10 --dataset cifar10 --num-class 10\
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 --seed 123\
  --arch resnet18 --moco_queue 8192 --prot_start 1 --lr 0.01 --wd 1e-3 --cosine --epochs 800\
  --loss_weight 0.5 --proto_m 0.99 --partial_rate 0.5

Run cifar100 with q=0.05

CUDA_VISIBLE_DEVICES=1 python -u train.py \
  --exp-dir experiment/PiCO-CIFAR-100 --dataset cifar100 --num-class 100\
  --dist-url 'tcp://localhost:10002' --multiprocessing-distributed --world-size 1 --rank 0 --seed 123\
  --arch resnet18 --moco_queue 8192 --prot_start 1 --lr 0.01 --wd 1e-3 --cosine --epochs 800\
  --loss_weight 0.5 --proto_m 0.99 --partial_rate 0.05

Run CUB200 with q=0.1

CUDA_VISIBLE_DEVICES=2 python -u train.py \
 --exp-dir experiment/PiCO-CUB --dataset cub200 --num-class 200\
 --dist-url 'tcp://localhost:10003' --multiprocessing-distributed --world-size 1 --rank 0 --seed 124\
 --arch resnet18 --moco_queue 4096 --prot_start 100 --lr 0.01 --wd 1e-5 --cosine --epochs 300\
 --batch-size 256 --loss_weight 0.5 --proto_m 0.99 --partial_rate 0.1

A Note on Running with Multiple-GPUs

Technically, we implemented PiCO using a distributed setup because we thought parallelization would be needed. However, while we train PiCO we had never actually enabled the distributed setup but only resorted to one single GPU training. Please carefully check the code if you would like to use multiple GPUs. It might be required to prepare a shared local copy of the partial label dataset.

Results

Main results on CIFAR-10 and CIFAR-100 datasets with varying q values. PiCO achieves SOTA results and is competitive to the supervised counterparts.