Adversarial Autoencoders

Overview

Adversarial Autoencoders (with Pytorch)

Dependencies

  • argparse
  • time
  • torch
  • torchvision
  • numpy
  • itertools
  • matplotlib

Create Datasets

python create_datasets.py
You might also like...
Re-implememtation of MAE (Masked Autoencoders Are Scalable Vision Learners) using PyTorch.

mae-repo PyTorch re-implememtation of "masked autoencoders are scalable vision learners". In this repo, it heavily borrows codes from codebase https:/

ConvMAE: Masked Convolution Meets Masked Autoencoders
ConvMAE: Masked Convolution Meets Masked Autoencoders

ConvMAE ConvMAE: Masked Convolution Meets Masked Autoencoders Peng Gao1, Teli Ma1, Hongsheng Li2, Jifeng Dai3, Yu Qiao1, 1 Shanghai AI Laboratory, 2 M

Code and pre-trained models for MultiMAE: Multi-modal Multi-task Masked Autoencoders
Code and pre-trained models for MultiMAE: Multi-modal Multi-task Masked Autoencoders

MultiMAE: Multi-modal Multi-task Masked Autoencoders Roman Bachmann*, David Mizrahi*, Andrei Atanov, Amir Zamir Website | arXiv | BibTeX Official PyTo

VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training [Arxiv] VideoMAE: Masked Autoencoders are Data-Efficient Learne

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Adversarial Training Against Location-Optimized Adversarial Patches arXiv | Paper | Code | Video | Slides Code for the paper: Sukrut Rao, David Stutz,

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

ACE Please find the preliminary version published at BMVC 2020 in the folder BMVC_version, and its extended journal version in Journal_version. Datase

transfer attack; adversarial examples; black-box attack; unrestricted Adversarial Attacks on ImageNet; CVPR2021 天池黑盒竞赛
transfer attack; adversarial examples; black-box attack; unrestricted Adversarial Attacks on ImageNet; CVPR2021 天池黑盒竞赛

transfer_adv CVPR-2021 AIC-VI: unrestricted Adversarial Attacks on ImageNet CVPR2021 安全AI挑战者计划第六期赛道2:ImageNet无限制对抗攻击 介绍 : 深度神经网络已经在各种视觉识别问题上取得了最先进的性能。

Adversarial-Information-Bottleneck - Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck (NeurIPS21)
Comments
  • AttributeError: can't set attribute ???

    AttributeError: can't set attribute ???

    Hi, when I run the code, I meet the following errors: "AttributeError: can't set attribute". and I checked the code of data pre-process script:

    trainset_new.train_data = train_data_sub.clone() trainset_new.train_labels = train_labels_sub.clone()

    but in the "sub.py" file, the class "subMNIST" do not define this attribute, how do you run the code successfully?

    opened by wangxiao5791509 8
  • Can z_dim get bigger? For example, 10, 128

    Can z_dim get bigger? For example, 10, 128

    When I tried the unsupervised experiment, I tried to improve the dimension of z_dim, but the generator was completely broken, and I failed to try to modify it. I wonder if you have tried, could you please provide some information? Thank you very much!

    opened by yasuobinggan 2
  • why you use these three numbers?

    why you use these three numbers?

    In aae_pytorch_basic.py, line 181 and line 212, those three constants are used for what? and how do you choose those? BTW, please forgive my poor english.... Thank you in advance.

    opened by Perfeee 1
  • `create_datasets.py` missing?

    `create_datasets.py` missing?

    The README mentions create_datasets.py -- I guess it is missing?

    Thanks v much for https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/, it is excellent.

    opened by jmmcd 1
Owner
Felipe Ducau
Felipe Ducau
Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun

ARAE Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun https://arxiv.org/abs/1706.04223 Disc

Junbo (Jake) Zhao 399 Jan 2, 2023
Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders

Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders

null 1 Oct 11, 2021
Data Augmentation with Variational Autoencoders

Documentation Pyraug This library provides a way to perform Data Augmentation using Variational Autoencoders in a reliable way even in challenging con

null 112 Nov 30, 2022
PyTorch Autoencoders - Implementing a Variational Autoencoder (VAE) Series in Pytorch.

PyTorch Autoencoders Implementing a Variational Autoencoder (VAE) Series in Pytorch. Inspired by this repository Model List check model paper conferen

Subin An 8 Nov 21, 2022
Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training

Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training Code for our paper "Predicting lncRNA–protein interactio

zhanglabNKU 1 Nov 29, 2022
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners This repository is built upon BEiT, thanks very much! Now, we on

Zhiliang Peng 2.3k Jan 4, 2023
PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-supervised ViT.

MAE for Self-supervised ViT Introduction This is an unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-sup

null 36 Oct 30, 2022
An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners

An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners This is a coarse version for MAE, only make the pretrain model, the fine

FlyEgle 214 Dec 29, 2022
A framework that constructs deep neural networks, autoencoders, logistic regressors, and linear networks

A framework that constructs deep neural networks, autoencoders, logistic regressors, and linear networks without the use of any outside machine learning libraries - all from scratch.

Kordel K. France 2 Nov 14, 2022
Autoencoders pretraining using clustering

Autoencoders pretraining using clustering

IITiS PAN 2 Dec 16, 2021