[CVPR 2026] This is the implementation of the paper: Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning.
Python 3.8.13
numpy 1.22.3
torch 1.10.0
torchvision 0.11.0
diffusers 0.28.2
To synthesize candidate labels, the annotation model weights should be downloaded from this link and place them into the ./partial_models/weights/ directory.
First, generate class-specific augmentations:
python -u csaugmentation.py --dataset cifar10
python -u csaugmentation.py --dataset cifar100
python -u csaugmentation.py --dataset pet37
python -u csaugmentation.py --dataset flower102
python -u csaugmentation.py --dataset fmnistThen, train the model:
python -u main.py --dataset cifar10
python -u main.py --dataset cifar100
python -u main.py --dataset pet37
python -u main.py --dataset flower102
python -u main.py --dataset fmnist