Code of paper: "DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks"

Overview

image

GitHub GitHub Repo stars GitHub Repo stars

DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks

Abstract: Adversarial training has been proven to be a powerful regularization method to improve generalization of models. In this work, a novel masked weight adversarial training method, DropAttack, is proposed for improving generalization potential of neural network models. It enhances the coverage and diversity of adversarial attack by intentionally adding worst-case adversarial perturbations to both the input and hidden layers and randomly masking the attack perturbations on a certain proportion weight parameters. It then improves the generalization of neural networks by minimizing the internal adversarial risk generated by exponentially different attack combinations. Further, the method is a general technique that can be adopted to a wide variety of neural networks with different architectures. To validate the effectiveness of the proposed method, five public datasets were used in the fields of natural language processing (NLP) and computer vision (CV) for experimental evaluating. This study compared DropAttack with other adversarial training methods and regularization methods. It was found that the proposed method achieves state-of-the-art performance on all datasets. In addition, the experimental results of this study show that DropAttack method can achieve similar performance when it uses only a half training data required in standard training. Theoretical analysis revealed that DropAttack can perform gradient regularization at random on some of the input and weight parameters of the model. Further, visualization experiments of this study show that DropAttack can push the minimum risk of the neural network model to a lower and flatter loss landscapes.

  • For technical details and additional experimental results, please refer to our paper:

“DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks”

image

  • Experimental results:

image

image

DropAttack indeed selects flatter loss landscapes via masked adversarial perturbations.

[The code of loss visualization] image

  • Citation

@article{ni2021dropattack,
  title={DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks},
  author={Ni, Shiwen and Li, Jiawen and Kao, Hung-Yu},
  journal={arXiv preprint arXiv:2108.12805},
  year={2021}
}
  • Requirements

pytorch
pandas
numpy
nltk
sklearn
torchtext
  • Please star it, thank you! :)

Owner
倪仕文 (Shiwen Ni)
PhD candidate in Computer Science (ML&NLP)
倪仕文 (Shiwen Ni)
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations

TopClus The source code used for Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations, published in WWW 2022. Requ

Yu Meng 63 Dec 18, 2022
This program uses trial auth token of Azure Cognitive Services to do speech synthesis for you.

🗣️ aspeak A simple text-to-speech client using azure TTS API(trial). 😆 TL;DR: This program uses trial auth token of Azure Cognitive Services to do s

Levi Zim 359 Jan 05, 2023
Pairwise Learning for Neural Link Prediction for OGB (PLNLP-OGB)

Pairwise Learning for Neural Link Prediction for OGB (PLNLP-OGB) This repository provides evaluation codes of PLNLP for OGB link property prediction t

Zhitao WANG 31 Oct 10, 2022
Image Super-Resolution by Neural Texture Transfer

SRNTT: Image Super-Resolution by Neural Texture Transfer Tensorflow implementation of the paper Image Super-Resolution by Neural Texture Transfer acce

Zhifei Zhang 413 Nov 30, 2022
(AAAI2020)Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing

Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing This repository contains pytorch source code for AAAI2020 oral paper: Grapy-ML

54 Aug 04, 2022
Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data 🌈

Rainbow 🌈 An implementation of Rainbow DQN which outperforms the paper's (Hessel et al. 2017) results on 40% of tested games while using 20x less dat

Dominik Schmidt 31 Dec 21, 2022
The source codes for ACL 2021 paper 'BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data'

BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data This repository provides the implementation details for

124 Dec 27, 2022
CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks

CALVIN CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks Oier Mees, Lukas Hermann, Erick Rosete,

Oier Mees 107 Dec 26, 2022
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation (ACM MM 2020)

Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation (ACM MM 2020) Official implementation of: Forest R-CNN: Large-Vo

Jialian Wu 54 Jan 06, 2023
Teaches a student network from the knowledge obtained via training of a larger teacher network

Distilling-the-knowledge-in-neural-network Teaches a student network from the knowledge obtained via training of a larger teacher network This is an i

Abhishek Sinha 146 Dec 11, 2022
Author's PyTorch implementation of TD3+BC, a simple variant of TD3 for offline RL

A Minimalist Approach to Offline Reinforcement Learning TD3+BC is a simple approach to offline RL where only two changes are made to TD3: (1) a weight

Scott Fujimoto 193 Dec 23, 2022
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022
[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Codes for [Preprint] Bag of Tricks for Training Deeper Graph

VITA 101 Dec 29, 2022
A vision library for performing sliced inference on large images/small objects

SAHI: Slicing Aided Hyper Inference A vision library for performing sliced inference on large images/small objects Overview Object detection and insta

Open Business Software Solutions 2.3k Jan 04, 2023
The mini-MusicNet dataset

mini-MusicNet A music-domain dataset for multi-label classification Music transcription is sequence-to-sequence prediction problem: given an audio per

John Thickstun 4 Nov 09, 2022
Simple node deletion tool for onnx.

snd4onnx Simple node deletion tool for onnx. I only test very miscellaneous and limited patterns as a hobby. There are probably a large number of bugs

Katsuya Hyodo 6 May 15, 2022
3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans.

3DMV 3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans. This work is based on our ECCV'18 p

Владислав Молодцов 0 Feb 06, 2022
Deep Image Matting implementation in PyTorch

Deep Image Matting Deep Image Matting paper implementation in PyTorch. Differences "fc6" is dropped. Indices pooling. "fc6" is clumpy, over 100 millio

Yang Liu 724 Dec 27, 2022
PyTorch code for the "Deep Neural Networks with Box Convolutions" paper

Box Convolution Layer for ConvNets Single-box-conv network (from `examples/mnist.py`) learns patterns on MNIST What This Is This is a PyTorch implemen

Egor Burkov 515 Dec 18, 2022
Code to reproduce the results in "Visually Grounded Reasoning across Languages and Cultures", EMNLP 2021.

marvl-code [WIP] This is the implementation of the approaches described in the paper: Fangyu Liu*, Emanuele Bugliarello*, Edoardo M. Ponti, Siva Reddy

25 Nov 15, 2022