A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation(DANN), support Office-31 and Office-Home dataset

Overview

DANN

A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation

Prerequisites

Linux or OSX

NVIDIA GPU + CUDA (may CuDNN) and corresponding PyTorch framework (version 0.3.1)

Python 2.7/3.5

Datasets

This responsity support Office-31 and Office-Home dataset

cd src
python train_DANN.py --gpu_id 0 --net ResNet50 --dset office --s_dset_path ../data/office/webcam_31_list.txt --t_dset_path ../data/office/amazon_31_list.txt --test_interval 500 --snapshot_interval 5000 --output_dir dann
You can set the command parameters to switch between different experiments. 
- "gpu_id" is the GPU ID to run experiments.
- "dset" parameter is the dataset selection. In our experiments, it can be "office" (for all the Office-31 tasks), "office-home" (for all the Office-Home tasks)
- "s_dset_path" is the source dataset list.
- "t_dset_path" is the target dataset list.
- "test_interval" is the interval of iterations between two test phase.
- "snapshot_interval" is the interval of iterations between two snapshot models.
- "output_dir" is the output directory of the log and snapshot.
- "net" sets the base network. For details of setting, you can see network.py.
    - For AlexNet, "net" is AlexNet.
    - For VGG, "net" is like VGG16. Detail names are in network.py.
    - For ResNet, "net" is like ResNet50. Detail names are in network.py.
pytorch implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802

PyTorch SRResNet Implementation of Paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"(https://arxiv.org/abs

Jiu XU 436 Jan 09, 2023
NaijaSenti is an open-source sentiment and emotion corpora for four major Nigerian languages

NaijaSenti is an open-source sentiment and emotion corpora for four major Nigerian languages. This project was supported by lacuna-fund initiatives. Jump straight to one of the sections below, or jus

Hausa Natural Language Processing 14 Dec 20, 2022
Scale-aware Automatic Augmentation for Object Detection (CVPR 2021)

SA-AutoAug Scale-aware Automatic Augmentation for Object Detection Yukang Chen, Yanwei Li, Tao Kong, Lu Qi, Ruihang Chu, Lei Li, Jiaya Jia [Paper] [Bi

DV Lab 182 Dec 29, 2022
AttentionGAN for Unpaired Image-to-Image Translation & Multi-Domain Image-to-Image Translation

AttentionGAN-v2 for Unpaired Image-to-Image Translation AttentionGAN-v2 Framework The proposed generator learns both foreground and background attenti

Hao Tang 530 Dec 27, 2022
Self-supervised Label Augmentation via Input Transformations (ICML 2020)

Self-supervised Label Augmentation via Input Transformations Authors: Hankook Lee, Sung Ju Hwang, Jinwoo Shin (KAIST) Accepted to ICML 2020 Install de

hankook 96 Dec 29, 2022
A modification of Daniel Russell's notebook merged with Katherine Crowson's hq-skip-net changes

Edits made to this repo by Katherine Crowson I have added several features to this repository for use in creating higher quality generative art (featu

Paul Fishwick 10 May 07, 2022
Source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals.

PatchGraph This repository contains the source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals. Installation Creat

Paloma Sodhi 11 Dec 15, 2022
Learning View Priors for Single-view 3D Reconstruction (CVPR 2019)

Learning View Priors for Single-view 3D Reconstruction (CVPR 2019) This is code for a paper Learning View Priors for Single-view 3D Reconstruction by

Hiroharu Kato 38 Aug 17, 2022
Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).

DINN We introduce Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, a

19 Dec 10, 2022
Official code of Team Yao at Multi-Modal-Fact-Verification-2022

Official code of Team Yao at Multi-Modal-Fact-Verification-2022 A Multi-Modal Fact Verification dataset released as part of the De-Factify workshop in

Wei-Yao Wang 11 Nov 15, 2022
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch

XMed-Lab 30 Sep 23, 2022
Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach

Introduction Datasets and source code for our paper Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach Datasets: WebFG-496

21 Sep 30, 2022
MAU: A Motion-Aware Unit for Video Prediction and Beyond, NeurIPS2021

MAU (NeurIPS2021) Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xinguang Xiang, Wen GAo. Official PyTorch Code for "MAU: A Motion-Aware

ZhengChang 20 Nov 25, 2022
JupyterLite demo deployed to GitHub Pages 🚀

JupyterLite Demo JupyterLite deployed as a static site to GitHub Pages, for demo purposes. ✨ Try it in your browser ✨ ➡️ https://jupyterlite.github.io

JupyterLite 223 Jan 04, 2023
Visual Question Answering in Pytorch

Visual Question Answering in pytorch /!\ New version of pytorch for VQA available here: https://github.com/Cadene/block.bootstrap.pytorch This repo wa

Remi 672 Jan 01, 2023
Simple image captioning model - CLIP prefix captioning.

CLIP prefix captioning. Inference Notebook: 🥳 New: 🥳 Our technical papar is finally out! Official implementation for the paper "ClipCap: CLIP Prefix

688 Jan 04, 2023
EMNLP 2020 - Summarizing Text on Any Aspects

Summarizing Text on Any Aspects This repo contains preliminary code of the following paper: Summarizing Text on Any Aspects: A Knowledge-Informed Weak

Bowen Tan 35 Nov 14, 2022
An open-source online reverse dictionary.

An open-source online reverse dictionary.

THUNLP 6.3k Jan 09, 2023
[CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing

Anycost GAN video | paper | website Anycost GANs for Interactive Image Synthesis and Editing Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zh

MIT HAN Lab 726 Dec 28, 2022
Create images and texts with the First Order Generative Adversarial Networks

First Order Divergence for training GANs This repository contains code accompanying the paper First Order Generative Advesarial Netoworks The majority

Zalando Research 35 Dec 11, 2021