facundocabrera/label_representations

[ICLR 2021] Beyond Categorical Label Representations for Image Classification

Beyond Categorical Label Representations for Image Classification

Boyuan Chen, Yu Li, Sunand Raghupathi, Hod Lipson
Columbia University
International Conference on Learning Representations (ICLR 2021)

Project Website | Video | Paper | Arxiv

Overview

This repo contains the PyTorch implementation for paper "Beyond Categorical Label Representations for Image Classification".

Content

• Installation
• Data Preparation
• About the labels
• Training
• Attacks
• Training with Less Data
• Shared Utilities

Installation

Create a python virtual environment and install the dependencies.

virtualenv -p /usr/bin/python3.6 env3.6
source env3.6/bin/activate
pip install -r requirements.txt

Data Preparation

Download the CIFAR10 and CIFAR100 datasets by running:

mkdir ./data
cd ./data
wget https://www.cs.toronto.edu/\~kriz/cifar-10-python.tar.gz
wget https://www.cs.toronto.edu/\~kriz/cifar-100-python.tar.gz
cd ..

About the labels

All labels are pre-generated in the labels folders and ready to be loaded directly for training. The notebook labels/labels.ipynb contains code for generating these labels.

Label types (--label):

• Category: category
• High-dimensional: speech, uniform, shuffle, composite, bert, and random
• Low-dimensional: lowdim and glove

Base model types (--model): vgg19, resnet32, and resnet110

Datasets (--dataset): cifar10 and cifar100 for cifar10 and cifar100

Seed (--seed): an int value for seeding data loading sequence

Data Level (--level): an int percentage (<90) for training with level% of all data (defaults to 100)

Base directory (--base_dir): location to save training/attacking results (required)

Label directory (--label_dir): location where label files are located (defaults to ./labels/label_files)

The labels/label_files folder contains the labels stored in .npy files.

• cifar10 high-dim labels:

• shape (10, 64, 64)

• dtype float32

• cifar100 high-dim labels:

• shape (100, 64, 64)

• dtype float32

You can find the original audio used to generate the speech labels in labels/cifar10_wav/ and labels/cifar100_wav/. You can view the grayscale images of all composite labels (rescaled to 0-255) in labels/composite/.

Training

Now to train the models use the following.

• Category: use train.py and specify label --label category

python3 train.py --model resnet110 --dataset cifar10 --seed 7 --label category

• High-dimensional: use train.py and specify a particular high-dimensional label

python3 train.py --model vgg19 --dataset cifar100 --seed 77 --label speech

Attacks

Run both targeted and untargeted FGSM and iterative attacks against trained models.

• Category: use attack.py

python3 attack.py --model resnet110 --dataset cifar10 --seed 7 --label category

• High-dimensional: use attack.py and specify a particular high-dimensional label

python3 attack.py --label speech --model vgg19 --dataset cifar100 --seed 77

Training with Less Data

Use the same training files as before, but specify a data level.

• Category: use train.py to train with 2% data

python3 train.py --model resnet110 --dataset cifar10 --seed 7 --label category --level 2

• High-dimensional: use train.py to train with 8% data

python3 train.py --model vgg19 --dataset cifar100 --seed 77 --label speech --level 8

Shared Utilities

• architecture.py: where the category and high-dimensional models are defined
• cifar.py: for loading cifar10 and cifar100 dataset for all label types
• utils/trainutil.py: other training helpers

BibTex

@inproceedings{chen2021beyond,
  title={Beyond Categorical Label Representations for Image Classification},
  author={Chen, Boyuan and Li, Yu and Raghupathi, Sunand and Lipson, Hod},
  booktitle={The International Conference on Learning Representations},
  year={2021}
}

License

This repository is released under the MIT license. See LICENSE for additional details.