kevinbro96 Goto Github PK

abba

auto_aug-master

awesome-data-augmentation

This is a list of awesome methods about data augmentation.

awesome-neural-art

A curated list of awesome neural network-based art resources.

awesome-person-re-identification

Awesome person re-identification

cd-vae

[NeurIPS 2021] "Class-Disentanglement and Applications in Adversarial Detection and Defense"

clae_vae

class_disentangle

conssl

PyTorch Implementation of SOTA SSL methods

csd

Implementation and datasets for Efficient Domain Generalization via Common-Specific Low-Rank Decomposition (https://arxiv.org/abs/2003.12815)

dal

Domain agnostic learning with disentangled representations

danet

Dual Attention Network for Scene Segmentation (CVPR2019)

ddan

deep-object-reid

OpenVINO Training Extensions Object Re-identification

deep-transfer-learning

A collection of implementations of deep domain adaptation algorithms

deep_mahalanobis_detector_subspace

Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".

domain-generalization

All about domain generalization

domainbed

DomainBed is a suite to test domain generalization algorithms

essentialmath

guided-denoise

The winning submission for NIPS 2017: Defense Against Adversarial Attack of team TSAIL

hcl

homework2-caltech101

icme2018_occluded-person-reidentification_datasets

Release the datasets in the paper（ ICME 2018 ） ,Occluded Person Re-identification.

jigendg

Repository for the CVPR19 oral paper "Domain Generalization by Solving Jigsaw Puzzles"

kevinbro96

Config files for my GitHub profile.

maml-pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

metabin

[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification

mmd_loss.pytorch

A pytorch implementation of Maximum Mean Discrepancies(MMD) loss

mmt

[ICLR-2020] Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification.

perceptual-advex

Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".