Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models

Related tags

Deep LearningLMPBT
Overview

LMPBT

Supplementary code for the Paper entitled ``Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models"

  1. Specification of dependencies Pytorch 1.7.1 torchvision 0.8.2 Scikit-learn 0.22 PyHessian (See, https://github.com/amirgholami/PyHessian.) pip install torch==1.7.1 torchvision==0.8.2 pip install pyhessian pip install scikit-learn

  2. Dataset MNIST, FASHION-MNIS, CIFAR-10, CIFAR-100, and SVHN : downloaded from torchvision.datasets CelebA : http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

  3. Training code We refer to the following code for model training : VAE : https://github.com/XavierXiao/Likelihood-Regret

To train the VAEs, use appropriate arguments and run this command: python train_vae.py

We refer to the following code for computing the low-rank-approximation of Hessian : https://github.com/amirgholami/PyHessian

To compute the top eigenvalues and eigenvectors of VAE models, run this command: python get_eig_vecs_vae.py

  1. Evaluation code To comput the LMPBT-scores using the VAE models, run this command: python get_lmpbt_score.py

  2. We provided the LMPBT socres of a VAE trained on CIFAR-100 and tested on CIFAR-100 as an in-distribution dataset, SVHN and CelebA as an OOD dataset. To comput the OOD performance metrics (AUROC, AUPR and FPR80) of these experiments, run this command: python get_metrics.py

  3. Pre-trained models We provided the pretrained VAE, trained on CIFAR-100.

The model can be loaded using the following code.

parser.add_argument('--state_E', default='./models/cifar100_netE.pth', help='path to encoder checkpoint')

parser.add_argument('--state_G', default='./models/cifar100_netG.pth', help='path to encoder checkpoint')

netG = DVAE.DCGAN_G(opt.imageSize, nz, nc, ngf, ngpu)

state_G = torch.load(opt.state_G, map_location=device)

netG.load_state_dict(state_G)

netE = DVAE.Encoder(opt.imageSize, nz, nc, ngf, ngpu)

state_E = torch.load(opt.state_E, map_location=device)

netE.load_state_dict(state_E)

This repository contains code and data for "On the Multimodal Person Verification Using Audio-Visual-Thermal Data"

trimodal_person_verification This repository contains the code, and preprocessed dataset featured in "A Study of Multimodal Person Verification Using

ISSAI 7 Aug 31, 2022
Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks This repository contains the code and data for the corresp

Friederike Metz 7 Apr 23, 2022
Code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”

GATER This repository contains the code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”. Our implementation is

Jiacheng Ye 12 Nov 24, 2022
Small-bets - Ergodic Experiment With Python

Ergodic Experiment Based on this video. Run this experiment with this command: p

Michael Brant 3 Jan 11, 2022
A new codebase for Group Activity Recognition. It contains codes for ICCV 2021 paper: Spatio-Temporal Dynamic Inference Network for Group Activity Recognition and some other methods.

Spatio-Temporal Dynamic Inference Network for Group Activity Recognition The source codes for ICCV2021 Paper: Spatio-Temporal Dynamic Inference Networ

40 Dec 12, 2022
Turning SymPy expressions into JAX functions

sympy2jax Turn SymPy expressions into parametrized, differentiable, vectorizable, JAX functions. All SymPy floats become trainable input parameters. S

Miles Cranmer 38 Dec 11, 2022
The source code of the paper "SHGNN: Structure-Aware Heterogeneous Graph Neural Network"

SHGNN: Structure-Aware Heterogeneous Graph Neural Network The source code and dataset of the paper: SHGNN: Structure-Aware Heterogeneous Graph Neural

Wentao Xu 7 Nov 13, 2022
Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)

IGNN Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" [paper] [supp] Prepare datasets 1 Download training dataset

Shangchen Zhou 278 Jan 03, 2023
WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose

WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose Yijun Zhou and James Gregson - BMVC2020 Abstract: We present an end-to-end head-pos

368 Dec 26, 2022
Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies

To make the comparison with Animatable NeRF easier on the Human3.6M dataset, we save the quantitative results at here, which also contains the results of other methods, including Neural Body, D-NeRF,

ZJU3DV 359 Jan 08, 2023
[ICCV2021] 3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds

3DVG-Transformer This repository is for the ICCV 2021 paper "3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds" Our method "3DV

22 Dec 11, 2022
Code for the USENIX 2017 paper: kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels

kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels Blazing fast x86-64 VM kernel fuzzing framework with performant VM reloads for Linux, MacOS an

Chair for Sys­tems Se­cu­ri­ty 541 Nov 27, 2022
Visyerres sgdf woob - Modules Woob pour l'intranet et autres sites Scouts et Guides de France

Vis'Yerres SGDF - Modules Woob Vous avez le sentiment que l'intranet des Scouts

Thomas Touhey (pas un pseudonyme) 3 Dec 24, 2022
Code of Puregaze: Purifying gaze feature for generalizable gaze estimation, AAAI 2022.

PureGaze: Purifying Gaze Feature for Generalizable Gaze Estimation Description Our work is accpeted by AAAI 2022. Picture: We propose a domain-general

39 Dec 05, 2022
Anatomy of Matplotlib -- tutorial developed for the SciPy conference

Introduction This tutorial is a complete re-imagining of how one should teach users the matplotlib library. Hopefully, this tutorial may serve as insp

Matplotlib Developers 1.1k Dec 29, 2022
Using a Seq2Seq RNN architecture via TensorFlow to predict future Bitcoin prices

Recurrent Bitcoin Network A Data Science Thesis Project About This repository contains the source code for implementing Bitcoin price prediciton using

Frizu 6 Sep 08, 2022
Pytorch implementation of VAEs for heterogeneous likelihoods.

Heterogeneous VAEs Beware: This repository is under construction 🛠️ Pytorch implementation of different VAE models to model heterogeneous data. Here,

Adrián Javaloy 35 Nov 29, 2022
Sub-tomogram-Detection - Deep learning based model for Cyro ET Sub-tomogram-Detection

Deep learning based model for Cyro ET Sub-tomogram-Detection High degree of stru

Siddhant Kumar 2 Feb 04, 2022
Universal Adversarial Triggers for Attacking and Analyzing NLP (EMNLP 2019)

Universal Adversarial Triggers for Attacking and Analyzing NLP This is the official code for the EMNLP 2019 paper, Universal Adversarial Triggers for

Eric Wallace 248 Dec 17, 2022
The Deep Learning with Julia book, using Flux.jl.

Deep Learning with Julia DL with Julia is a book about how to do various deep learning tasks using the Julia programming language and specifically the

Logan Kilpatrick 67 Dec 25, 2022