[NeurIPS 2022 Spotlight] GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

Out-of-distribution detection, robustness, and generalization resources. The repository contains a professionally curated list of papers, tutorials, books, videos, articles and open-source librarie…

LLM101n: Let's build a Storyteller

《迁移学习简明手册》LaTex源码

Python implementation of simple GMM and HMM models for isolated digit recognition.

PyCIL: A Python Toolbox for Class-Incremental Learning

Routines for exploratory data analysis.

Python Library for Signal/Image Data Analysis with Transport Methods

Python of "Entropic Wasserstein Component Analysis" paper

ICASSP 2023-2024 Papers: A complete collection of influential and exciting research papers from the ICASSP 2023-24 conferences. Explore the latest advancements in acoustics, speech and signal proce…

TorchDR - PyTorch Dimensionality Reduction

Incremental Learning of Gaussian Mixture Models (IGMM)

modular domain generalization: https://pypi.org/project/domainlab/

PyTorch implementation of "Wasserstein Iterative Networks for Barycenter Estimation" (NeurIPS 2022)

Bearing fault diagnosis model based on MCNN-LSTM

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

This repository is for the transfer learning or domain adaptive with fault diagnosis.

Fault Diagnosis Employing Transfer Learning Techniques: Domain Adaptation and Domain Generalization

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

A simple tutorial of Diffusion Probabilistic Models

This code was finished before source code hasn't been released.

Code for Transfer Learning book--《迁移学习导论》配套代码

Domain adaptation toolbox compatible with scikit-learn and pytorch

Approximative algorithms for computing sparse Wasserstein-2 multi-marginal optimal transport plans and associated free support barycenters.

Approximative algorithms for free-support Wasserstein-2 barycenters of discrete probability distributions.

Coresets for Wasserstein Distributionally Robust Optimization Problems

Draw datasets from within Jupyter.

Domain Aggregation Networks