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
Density-aware Single Image De-raining using a Multi-stream Dense Network (CVPR 2018)

DID-MDN Density-aware Single Image De-raining using a Multi-stream Dense Network He Zhang, Vishal M. Patel [Paper Link] (CVPR'18) We present a novel d

He Zhang 224 Dec 12, 2022
Keras-retinanet - Keras implementation of RetinaNet object detection.

Keras RetinaNet Keras implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal,

Fizyr 4.3k Jan 01, 2023
A deep-learning pipeline for segmentation of ambiguous microscopic images.

Welcome to Official repository of deepflash2 - a deep-learning pipeline for segmentation of ambiguous microscopic images. Quick Start in 30 seconds se

Matthias Griebel 39 Dec 19, 2022
Pytorch implementation of the paper SPICE: Semantic Pseudo-labeling for Image Clustering

SPICE: Semantic Pseudo-labeling for Image Clustering By Chuang Niu and Ge Wang This is a Pytorch implementation of the paper. (In updating) SOTA on 5

Chuang Niu 154 Dec 15, 2022
What can linearized neural networks actually say about generalization?

What can linearized neural networks actually say about generalization? This is the source code to reproduce the experiments of the NeurIPS 2021 paper

gortizji 11 Dec 09, 2022
Code for our paper "SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization", ACL 2021

SimCLS Code for our paper: "SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization", ACL 2021 1. How to Install Requirements

Yixin Liu 150 Dec 12, 2022
Galaxy images labelled by morphology (shape). Aimed at ML development and teaching

Galaxy images labelled by morphology (shape). Aimed at ML debugging and teaching.

Mike Walmsley 14 Nov 28, 2022
MonoScene: Monocular 3D Semantic Scene Completion

MonoScene: Monocular 3D Semantic Scene Completion MonoScene: Monocular 3D Semantic Scene Completion] [arXiv + supp] | [Project page] Anh-Quan Cao, Rao

298 Jan 08, 2023
Embodied Intelligence via Learning and Evolution

Embodied Intelligence via Learning and Evolution This is the code for the paper Embodied Intelligence via Learning and Evolution Agrim Gupta, Silvio S

Agrim Gupta 111 Dec 13, 2022
IEEE Winter Conference on Applications of Computer Vision 2022 Accepted

SSKT(Accepted WACV2022) Concept map Dataset Image dataset CIFAR10 (torchvision) CIFAR100 (torchvision) STL10 (torchvision) Pascal VOC (torchvision) Im

1 Nov 17, 2022
Pytorch GUI(demo) for iVOS(interactive VOS) and GIS (Guided iVOS)

GUI for iVOS(interactive VOS) and GIS (Guided iVOS) GUI Implementation of CVPR2021 paper "Guided Interactive Video Object Segmentation Using Reliabili

Yuk Heo 13 Dec 09, 2022
Adaptive Denoising Training (ADT) for Recommendation.

DenoisingRec Adaptive Denoising Training for Recommendation. This is the pytorch implementation of our paper at WSDM 2021: Denoising Implicit Feedback

Wenjie Wang 51 Dec 30, 2022
Kaggle competition: Springleaf Marketing Response

PruebaEnel Prueba Kaggle-Springleaf-master Prueba Kaggle-Springleaf Kaggle competition: Springleaf Marketing Response Competencia de Kaggle: Marketing

1 Feb 09, 2022
Code for ICCV 2021 paper "HuMoR: 3D Human Motion Model for Robust Pose Estimation"

Code for ICCV 2021 paper "HuMoR: 3D Human Motion Model for Robust Pose Estimation"

Davis Rempe 367 Dec 24, 2022
NFNets and Adaptive Gradient Clipping for SGD implemented in PyTorch

PyTorch implementation of Normalizer-Free Networks and SGD - Adaptive Gradient Clipping Paper: https://arxiv.org/abs/2102.06171.pdf Original code: htt

Vaibhav Balloli 320 Jan 02, 2023
Simulation of moving particles under microscopic imaging

Simulation of moving particles under microscopic imaging Install scipy numpy scikit-image tiffile Run python simulation.py Read result https://imagej

Zehao Wang 2 Dec 14, 2021
A criticism of a recent paper on buggy image downsampling methods in popular image processing and deep learning libraries.

A criticism of a recent paper on buggy image downsampling methods in popular image processing and deep learning libraries.

70 Jul 12, 2022
Implementation for Paper "Inverting Generative Adversarial Renderer for Face Reconstruction"

StyleGAR TODO: add arxiv link Implementation of Inverting Generative Adversarial Renderer for Face Reconstruction TODO: for test Currently, some model

155 Oct 27, 2022
structured-generative-modeling

This repository contains the implementation for the paper Information Theoretic StructuredGenerative Modeling, Specially thanks for the open-source co

0 Oct 11, 2021
[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation [Paper] Prerequisites To install requirements: pip install -r requirements.txt

Guangrui Li 84 Dec 26, 2022