jiazhi412 Goto Github PK

attend2u

🖼️ Attend to You: Personalized Image Captioning with Context Sequence Memory Networks. In CVPR, 2017. Expanded : Towards Personalized Image Captioning via Multimodal Memory Networks. In IEEE TPAMI, 2018.

attgan-pytorch

AttGAN PyTorch Arbitrary Facial Attribute Editing: Only Change What You Want

balanced-datasets-are-not-enough

[ICCV 2019] Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

bashrc

bias-contrastive-learning

Official Pytorch implementation of "Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021)

coding-interview-university

A complete computer science study plan to become a software engineer.

counterfactual_generative_networks

[ICLR'21] Counterfactual Generative Networks

csad

csci566-assignment1

CSCI-566 Assignment 1

dataset-repair

REPresentAtion bIas Removal (REPAIR) of datasets

dcgan-tensorflow

A Tensorflow implementation of Deep Convolutional Generative Adversarial Networks trained on Fashion-MNIST, CIFAR-10, etc.

debface

deep-learning

digital-image-processing

USC EE569

domainbiasmitigation

econ

ee499_ml_spring23

entangling-disentangling-bias

EnD: Entangling and Disentangling deep representations for bias correction | CVPR21 https://doi.org/10.1109/CVPR46437.2021.01330

fairface

fairface_feature

fake-image-detection

ffhq-aging-dataset

FFHQ-Aging Dataset

ffhq-dataset

Flickr-Faces-HQ Dataset (FFHQ)

gan-debiasing

Fair Attribute Classification through Latent Space De-biasing (CVPR 2021)

ganhacks

starter from "How to Train a GAN?" at NIPS2016

group_dro

Distributionally robust neural networks for group shifts

infogan-pytorch

PyTorch Implementation of InfoGAN

jiazhi412.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

learn-unbiased

Code for the paper Learning Unbiased Representations via Mutual Information Backpropagation

learning-not-to-learn

[CVPR2019]Learning Not to Learn : An adversarial method to train deep neural networks with biased data