Projects sharing features with Moving Semantic Transfer Network

SBADA-GAN CVPR 2018 code This is a preliminary release, as the code needs a massive cleanup being extremely verbose in this current state. In the Mnist_MnistM notebook can be found an example of how to run SBADA-GAN on Mnist -> MnistM Domain Adaptation task.

This code requires Python 3, and is implemented in Tensorflow.

pytorch implementation of Domain-Adversarial Training of Neural Networks

This code was developed based on dirt-t.

Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling

Code release for Discriminative Adversarial Domain Adaptation (AAAI2020).

Domain Adaptation Based on the Triplet Loss

(ECCV 2020) Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation

A collection of implementations of adversarial domain adaptation algorithms

Cycle-consistent Conditional Adversarial Transfer Networks, ACM MM 2019

Codes for "Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation" in CVPR 2021

Unsupervised Domain Adaptation for Computer Vision Tasks

Unofficial pytorch implementation of algorithms for domain adaptation

Official implementation of Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation presented at ICCV 2019.

Code for the paper "Adversarial Feature Augmentation for Unsupervised Domain Adaptation", CVPR 2018

Tensorflow version: 1.3.0

A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)

This repository serves as a centralized collection of state-of-the-art deep learning architectures and reference implementations designed for research and application development. It provides a comprehensive toolkit for computer vision and natural language processing, offering pre-built models and training pipelines for tasks ranging from image classification and object detection to complex sequence modeling. The project distinguishes itself by providing a flexible execution harness that manages the entire training lifecycle, including data ingestion and backpropagation. It supports scalable

Code for "Cluster Alignment with a Teacher for Unsupervised Domain Adaptation"

Code release for Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation (ICML 2019)

Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

Code release for "Multi-Adversarial Domain Adaptation" (AAAI 2018)

Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)

ICML 2022Source code for "A Closer Look at Smoothness in Domain Adversarial Training",

CVPR2022 Official implementation of DALN.