Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"

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Deep Learning Learning-Debiased-Disentangled
Overview

Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral): Official Project Webpage

This repository provides the official PyTorch implementation of the following paper:

Learning Debiased Representation via Disentangled Feature Augmentation
Jungsoo Lee* (KAIST AI, Kakao Enterprise), Eungyeup Kim* (KAIST AI, Kakao Enterprise),
Juyoung Lee (Kakao Enterprise), Jihyeon Lee (KAIST AI), and Jaegul Choo (KAIST AI)
(* indicates equal contribution. The order of first authors was chosen by tossing a coin.)
NeurIPS 2021, Oral

Paper: Arxiv

Abstract: Image classification models tend to make decisions based on peripheral attributes of data items that have strong correlation with a target variable (i.e., dataset bias). These biased models suffer from the poor generalization capability when evaluated on unbiased datasets. Existing approaches for debiasing often identify and emphasize those samples with no such correlation (i.e., bias-conflicting) without defining the bias type in advance. However, such bias-conflicting samples are significantly scarce in biased datasets, limiting the debiasing capability of these approaches. This paper first presents an empirical analysis revealing that training with "diverse" bias-conflicting samples beyond a given training set is crucial for debiasing as well as the generalization capability. Based on this observation, we propose a novel feature-level data augmentation technique in order to synthesize diverse bias-conflicting samples. To this end, our method learns the disentangled representation of (1) the intrinsic attributes (i.e., those inherently defining a certain class) and (2) bias attributes (i.e., peripheral attributes causing the bias), from a large number of bias-aligned samples, the bias attributes of which have strong correlation with the target variable. Using the disentangled representation, we synthesize bias-conflicting samples that contain the diverse intrinsic attributes of bias-aligned samples by swapping their latent features. By utilizing these diversified bias-conflicting features during the training, our approach achieves superior classification accuracy and debiasing results against the existing baselines on both synthetic as well as a real-world dataset.

Code Contributors

Jungsoo Lee [Website] [LinkedIn] [Google Scholar] (KAIST AI, Kakao Enterprise)
Eungyeup Kim [Website] [LinkedIn] [Google Scholar] (KAIST AI, Kakao Enterprise)
Juyoung Lee [Website] (Kakao Enterprise)

Pytorch Implementation

Installation

Clone this repository.

git clone https://github.com/kakaoenterprise/Learning-Debiased-Disentangled.git
cd Learning-Debiased-Disentangled
pip install -r requirements.txt

Datasets

We used three datasets in our paper.

Download the datasets with the following url. Note that BFFHQ is the dataset used in "BiaSwap: Removing Dataset Bias with Bias-Tailored Swapping Augmentation" (Kim et al., ICCV 2021). Unzip the files and the directory structures will be as following:

cmnist
 └ 0.5pct / 1pct / 2pct / 5pct
     └ align
     └ conlict
     └ valid
 └ test

cifar10c
 └ 0.5pct / 1pct / 2pct / 5pct
     └ align
     └ conlict
     └ valid
 └ test

bffhq
 └ 0.5pct
 └ valid
 └ test

How to Run

CMNIST

Vanilla
python train.py --dataset cmnist --exp=cmnist_0.5_vanilla --lr=0.01 --percent=0.5pct --train_vanilla --tensorboard --wandb
python train.py --dataset cmnist --exp=cmnist_1_vanilla --lr=0.01 --percent=1pct --train_vanilla --tensorboard --wandb
python train.py --dataset cmnist --exp=cmnist_2_vanilla --lr=0.01 --percent=2pct --train_vanilla --tensorboard --wandb
python train.py --dataset cmnist --exp=cmnist_5_vanilla --lr=0.01 --percent=5pct --train_vanilla --tensorboard --wandb

bash scripts/run_cmnist_vanilla.sh
Ours
python train.py --dataset cmnist --exp=cmnist_0.5_ours --lr=0.01 --percent=0.5pct --curr_step=10000 --lambda_swap=1 --lambda_dis_align=10 --lambda_swap_align=10 --use_lr_decay --lr_decay_step=10000 --lr_gamma=0.5 --train_ours --tensorboard --wandb
python train.py --dataset cmnist --exp=cmnist_1_ours --lr=0.01 --percent=1pct  --curr_step=10000 --lambda_swap=1 --lambda_dis_align=10 --lambda_swap_align=10 --use_lr_decay --lr_decay_step=10000 --lr_gamma=0.5 --train_ours --tensorboard --wandb
python train.py --dataset cmnist --exp=cmnist_2_ours --lr=0.01 --percent=2pct  --curr_step=10000 --lambda_swap=1 --lambda_dis_align=10 --lambda_swap_align=10 --use_lr_decay --lr_decay_step=10000 --lr_gamma=0.5 --train_ours --tensorboard --wandb
python train.py --dataset cmnist --exp=cmnist_5_ours --lr=0.01 --percent=5pct  --curr_step=10000 --lambda_swap=1 --lambda_dis_align=10 --lambda_swap_align=10 --use_lr_decay --lr_decay_step=10000 --lr_gamma=0.5 --train_ours --tensorboard --wandb

bash scripts/run_cmnist_ours.sh

Corrupted CIFAR10

Vanilla
python train.py --dataset cifar10c --exp=cifar10c_0.5_vanilla --lr=0.001 --percent=0.5pct --train_vanilla --tensorboard --wandb
python train.py --dataset cifar10c --exp=cifar10c_1_vanilla --lr=0.001 --percent=1pct --train_vanilla --tensorboard --wandb
python train.py --dataset cifar10c --exp=cifar10c_2_vanilla --lr=0.001 --percent=2pct --train_vanilla --tensorboard --wandb
python train.py --dataset cifar10c --exp=cifar10c_5_vanilla --lr=0.001 --percent=5pct --train_vanilla --tensorboard --wandb

bash scripts/run_cifar10c_vanilla.sh
Ours
python train.py --dataset cifar10c --exp=cifar10c_0.5_ours --lr=0.0005 --percent=0.5pct --curr_step=10000 --lambda_swap=1 --lambda_dis_align=1 --lambda_swap_align=1 --use_lr_decay --lr_decay_step=10000 --lr_gamma=0.5 --train_ours --tensorboard --wandb
python train.py --dataset cifar10c --exp=cifar10c_1_ours --lr=0.001 --percent=1pct --curr_step=10000 --lambda_swap=1 --lambda_dis_align=5 --lambda_swap_align=5 --use_lr_decay --lr_decay_step=10000 --lr_gamma=0.5 --train_ours --tensorboard --wandb
python train.py --dataset cifar10c --exp=cifar10c_2_ours --lr=0.001 --percent=2pct --curr_step=10000 --lambda_swap=1 --lambda_dis_align=5 --lambda_swap_align=5 --use_lr_decay --lr_decay_step=10000 --lr_gamma=0.5 --train_ours --tensorboard --wandb
python train.py --dataset cifar10c --exp=cifar10c_5_ours --lr=0.001 --percent=5pct --curr_step=10000 --lambda_swap=1 --lambda_dis_align=1 --lambda_swap_align=1 --use_lr_decay --lr_decay_step=10000 --lr_gamma=0.5 --train_ours --tensorboard --wandb

bash scripts/run_cifar10c_ours.sh

BFFHQ

Vanilla
python train.py --dataset bffhq --exp=bffhq_0.5_vanilla --lr=0.0001 --percent=0.5pct --train_vanilla --tensorboard --wandb

bash scripts/run_bffhq_vanilla.sh
Ours
python train.py --dataset bffhq --exp=bffhq_0.5_ours --lr=0.0001 --percent=0.5pct --lambda_swap=0.1 --curr_step=10000 --use_lr_decay --lr_decay_step=10000 --lambda_dis_align 2. --lambda_swap_align 2. --dataset bffhq --train_ours --tensorboard --wandb

bash scripts/run_bffhq_ours.sh

Pretrained Models

In order to test our pretrained models, run the following command.

python test.py --pretrained_path=
   
     --dataset=
    
      --percent=

We provide the pretrained models in the following urls.
CMNIST 0.5pct
CMNIST 1pct
CMNIST 2pct
CMNIST 5pct

CIFAR10C 0.5pct
CIFAR10C 1pct
CIFAR10C 2pct
CIFAR10C 5pct

BFFHQ 0.5pct

Citations

Bibtex coming soon!

Contact

Jungsoo Lee

Eungyeup Kim

Juyoung Lee

Kakao Enterprise/Vision Team

Acknowledgments

This work was mainly done when both of the first authors were doing internship at Vision Team/AI Lab/Kakao Enterprise. Our pytorch implementation is based on LfF. Thanks for the implementation.

Comments
  • Official method name

    Official method name

    Dear authors, What's the official name for your method? I cannot find the name in your paper. I want to compare your method in my paper. Thanks in advance.

    Shaohua

    opened by googlebaba 1
  • Intuition on GCE

    Intuition on GCE

    Hello dear authors,

    I was wondering if I could have some clarifications on GCE.

    1. GCE is almost an interpolation between CCE and MAE. Their respective derivates are the following: image
    2. While CCE dynamically attributes different "weight" (1/f(x)) according to the difficulty of the sample, MAE is uniform across all samples.
    3. Usually, "difficult" samples are bias-conflicting samples. Thus, the gradient is higher CCE for this scenario.
    4. On the other hand, GCE attributes less gradient in the same scenario. Thus, GCE is less attentive to "difficult" samples, which in turn allocates more attention to "easier" samples in expectation. Therefore, GCE is less focused on intrinsic attributes and seeks after shortcuts

    The reason I doubt my statement is due to your clarifications on GCE in OpenReview. image I thought GCE was more attentive to the bias element not because it emphasizes the easy samples but de-emphasizes the hard ones.

    Thank you for your attention and great paper!

    opened by josejhlee 1
  • Code for data generating

    Code for data generating

    Hi, Thanks for open-sourced the code! Can you please make the data generating code public? I hope to further modify the dataset to test the relationship between bias rate and performance. Your code seems clear from the released dataset, I would love to refer to it.

    opened by brave-cattle 1
  • Non-decreasing Training Loss

    Non-decreasing Training Loss

    Hi,

    Thanks for open-sourced the code! I made a local copy of the code base as well as the CMNIST dataset. Simply by running the model of MLP and MLP_disentangled with provided Linux command lines as instructed in README. The training loss of either case is not decreasing and hence the test accuracy is similar with a naive classifier (~10%). Could you look into this issue?

    Thank you!

    opened by GaoxiangLuo 1
  • A question about the experiment result.

    A question about the experiment result.

    When I run the command

    python train.py --dataset cmnist --exp=cmnist_0.5_ours --lr=0.01 --percent=0.5pct --curr_step=10000 --lambda_swap=1 --lambda_dis_align=10 --lambda_swap_align=10 --use_lr_decay --lr_decay_step=10000 --lr_gamma=0.5 --train_ours --tensorboard

    the output is

    ...
valid_d: 0.11319999396800995 || test_d: 0.11349999904632568 
 97%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▌   | 48496/50000 [12:32<00:16, 88.77it/s
48500 model saved ...                                                                                                                                         
valid_d: 0.11319999396800995 || test_d: 0.11349999904632568 
 98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▋  | 48996/50000 [12:40<00:10, 96.10it/s]
49000 model saved ...
                                                                                                                                                            
49000 model saved ...                                                                                                                                         
valid_d: 0.11319999396800995 || test_d: 0.11349999904632568 
 99%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 49500/50000 [12:49<00:07, 71.10it/s
49500 model saved ...                                                                                                                                         
valid_d: 0.11319999396800995 || test_d: 0.11349999904632568

    The test acc is 0.1135. However in the paper, the result is 65.22. I don't know what's wrong. Can anyone help me?

    opened by lileicv 1
  • Validation set for CMNIST

    Validation set for CMNIST

    Dear authors, I want to know the data setting of validation set for CMNIST. Does the validation set have the same bias degree with training set? Thanks in advance.

    opened by googlebaba 5
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Kakao Enterprise Corp.
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