Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Automated Machine Learning with scikit-learn

🦞 Just talk to your agent — it learns and EVOLVES 🧬.

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.

A Production-ready Reinforcement Learning AI Agent Library brought by the Applied Reinforcement Learning team at Meta.

A PyTorch Library for Meta-learning Research

Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

Implementation of papers in 100 lines of code.

Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

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

Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning

FSL-Mate: A collection of resources for few-shot learning (FSL).

Meta-Transformer for Unified Multimodal Learning

A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.

Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.

Repository for few-shot learning machine learning projects

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)

[TPAMI 2023] LibFewShot: A Comprehensive Library for Few-shot Learning.

Code for the paper "On First-Order Meta-Learning Algorithms"

Collection for Few-shot Learning

Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) paper in Pytorch.

A dataset of datasets for learning to learn from few examples

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)

All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.

Manipulating Python Programs