PyGDA is a Python library for Graph Domain Adaptation.

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

SAM: Sharpness-Aware Minimization (PyTorch)

Unsupervised Multi-source Domain Adaptation Without Access to Source Data (CVPR '21 Oral)

Deep Attentive Center Loss

Code refactoring for paper "Domain Generalization with Adversarial Feature Learning" in VLCS datasets.

Open set domain adaptation code DAOD

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

Deep domain adaptation networks (DDAN) library for Python with TensorFlow.

A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition

CrossTrainer: Practical Domain Adaptation with Loss Reweighting

Unsupervised Domain Adaptation for Acoustic Scene Classification with Wasserstein Distance

PyTorch reimplementation of Moving Semantic Transfer Network

Quick implementation of Deep Cosine Metric Learning for Person Re-Identification in Keras

PyTorch implementation of CCSA("Unified Deep Supervised Domain Adaptation and Generalization")

Code release for "Unsupervised Domain Adaptation: An Adaptive Feature Norm Approach".

A Pytorch implementation of center loss on MNIST

Simple Tensorflow implementation of Batch-Instance Normalization (NIPS 2018)

Code for the paper Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation

Set of 1D CNN models to classify sound clips from the Urban Sound Classification dataset using Keras and Librosa

Toy version of Paillier cryptosystem

An inplementation of vggish in keras with tf backend

A Tensorflow implementation of paper, Deep CORAL: Correlation Alignment for Deep Domain Adaptation