ReAct: Out-of-distribution Detection With Rectified Activations

Related tags

Deep Learningreact
Overview

ReAct: Out-of-distribution Detection With Rectified Activations

This is the source code for paper ReAct: Out-of-distribution Detection With Rectified Activations by Yiyou Sun, Chuan Guo and Yixuan Li.

In this work, we propose ReAct—a simple technique for reducing model overconfidence on OOD data. Our method is motivated by novel analysis on internal activations of neural networks, which displays highly distinctive signature patterns for most OOD distributions.

Usage

1. Dataset Preparation

In-distribution dataset

Please download ImageNet-1k and place the training data and validation data in ./datasets/id_data/ILSVRC-2012/train and ./datasets/id_data/ILSVRC-2012/val, respectively.

Out-of-distribution dataset

We have curated 4 OOD datasets from iNaturalist, SUN, Places, and Textures, and de-duplicated concepts overlapped with ImageNet-1k.

For iNaturalist, SUN, and Places, we have sampled 10,000 images from the selected concepts for each dataset, which can be download via the following links:

wget http://pages.cs.wisc.edu/~huangrui/imagenet_ood_dataset/iNaturalist.tar.gz
wget http://pages.cs.wisc.edu/~huangrui/imagenet_ood_dataset/SUN.tar.gz
wget http://pages.cs.wisc.edu/~huangrui/imagenet_ood_dataset/Places.tar.gz

For Textures, we use the entire dataset, which can be downloaded from their original website.

Please put all downloaded OOD datasets into ./datasets/ood_data/.

2. Pre-trained Model Preparation

The model we used in the paper is the pre-trained ResNet-50 and MobileNet-v2 provided by Pytorch. The download process will start upon running.

3. OOD Detection Evaluation

To reproduce our results on ResNet-50, please run:

python eval.py --threshold 1.0

To reproduce baseline approaches (Energy Score), please run:

python eval.py --threshold 1e6  #we set the threshold close to infinity, so it is the original energy score.

OOD Detection Results

ReACT achieves state-of-the-art performance averaged on the 4 OOD datasets.

results

Citation

If you use our codebase, please cite our work:

@inproceedings{sun2021react,
  title={ReAct: Out-of-distribution Detection With Rectified Activations},
  author={Sun, Yiyou and Guo, Chuan and Li, Yixuan},
  booktitle={Advances in Neural Information Processing Systems},
  year={2021}
}
Owner
CS Research Group led by Prof. Sharon Li
CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable.

CausalNLP CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable. Install pip install -U

Arun S. Maiya 95 Jan 03, 2023
PyTorch implementation for the ICLR 2020 paper "Understanding the Limitations of Variational Mutual Information Estimators"

Smoothed Mutual Information ``Lower Bound'' Estimator PyTorch implementation for the ICLR 2020 paper Understanding the Limitations of Variational Mutu

50 Nov 09, 2022
Official PyTorch Implementation for InfoSwap: Information Bottleneck Disentanglement for Identity Swapping

InfoSwap: Information Bottleneck Disentanglement for Identity Swapping Code usage Please check out the user manual page. Paper Gege Gao, Huaibo Huang,

Grace Hešeri 56 Dec 20, 2022
Python package for covariance matrices manipulation and Biosignal classification with application in Brain Computer interface

pyRiemann pyRiemann is a python package for covariance matrices manipulation and classification through Riemannian geometry. The primary target is cla

447 Jan 05, 2023
Implementation of "Semi-supervised Domain Adaptive Structure Learning"

Semi-supervised Domain Adaptive Structure Learning - ASDA This repo contains the source code and dataset for our ASDA paper. Illustration of the propo

3 Dec 13, 2021
SAMO: Streaming Architecture Mapping Optimisation

SAMO: Streaming Architecture Mapping Optimiser The SAMO framework provides a method of optimising the mapping of a Convolutional Neural Network model

Alexander Montgomerie-Corcoran 20 Dec 10, 2022
DeepFaceLive - Live Deep Fake in python, Real-time face swap for PC streaming or video calls

DeepFaceLive - Live Deep Fake in python, Real-time face swap for PC streaming or video calls

8.3k Dec 31, 2022
This repo is to present various code demos on how to use our Graph4NLP library.

Deep Learning on Graphs for Natural Language Processing Demo The repository contains code examples for DLG4NLP tutorials at NAACL 2021, SIGIR 2021, KD

Graph4AI 143 Dec 23, 2022
Structured Edge Detection Toolbox

################################################################### # # # Structure

Piotr Dollar 779 Jan 02, 2023
CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery

CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery This paper (CoANet) has been published in IEEE TIP 2021. This code i

Jie Mei 53 Dec 03, 2022
A TensorFlow implementation of FCN-8s

FCN-8s implementation in TensorFlow Contents Overview Examples and demo video Dependencies How to use it Download pre-trained VGG-16 Overview This is

Pierluigi Ferrari 50 Aug 08, 2022
DECAF: Deep Extreme Classification with Label Features

DECAF DECAF: Deep Extreme Classification with Label Features @InProceedings{Mittal21, author = "Mittal, A. and Dahiya, K. and Agrawal, S. and Sain

46 Nov 06, 2022
This is the latest version of the PULP SDK

PULP-SDK This is the latest version of the PULP SDK, which is under active development. The previous (now legacy) version, which is no longer supporte

78 Dec 07, 2022
Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

Kento Nishi 22 Jul 07, 2022
A clean and extensible PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

A clean and extensible PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners A PyTorch re-implementation of Mask Autoencoder trai

Tianyu Hua 23 Dec 13, 2022
Sequential Model-based Algorithm Configuration

SMAC v3 Project Copyright (C) 2016-2018 AutoML Group Attention: This package is a reimplementation of the original SMAC tool (see reference below). Ho

AutoML-Freiburg-Hannover 778 Jan 05, 2023
code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

PreSumm This code is for EMNLP 2019 paper Text Summarization with Pretrained Encoders Updates Jan 22 2020: Now you can Summarize Raw Text Input!. Swit

Yang Liu 1.2k Dec 28, 2022
Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17

2017 VQA Challenge Winner (CVPR'17 Workshop) pytorch implementation of Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challeng

Mark Dong 166 Dec 11, 2022
Mall-Customers-Segmentation - Customer Segmentation Using K-Means Clustering

Overview Customer Segmentation is one the most important applications of unsupervised learning. Using clustering techniques, companies can identify th

NelakurthiSudheer 2 Jan 03, 2022
Raindrop strategy for Irregular time series

Graph-Guided Network For Irregularly Sampled Multivariate Time Series Overview This repository contains processed datasets and implementation code for

Zitnik Lab @ Harvard 74 Jan 03, 2023