104 Open source projects that are alternatives of or similar to Meta Weight Net

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..

Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark

Codes and datasets for AAAI-2021 paper "Learning to Pre-train Graph Neural Networks"

Automated Machine Learning with scikit-learn

Official repository of https://arxiv.org/abs/2111.04040v1

Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

This repo provides pytorch code which replicates the results of the Matching Networks for One Shot Learning paper on the Omniglot and MiniImageNet dataset

A modular toolbox for meta-learning research with a focus on speed and reproducibility.

No description or website provided.

Code accompanying the ICML-2018 paper "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace"

Repository for few-shot learning machine learning projects

Repository containing code for the paper "Meta-Learning with Sparse Experience Replay for Lifelong Language Learning".

A PyTorch Library for Meta-learning Research

A dataset of datasets for learning to learn from few examples

Bilevel Optimization Library in Python for Multi-Task and Meta Learning

Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)

Graph meta learning via local subgraphs (NeurIPS 2020)

Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020

My personal toolkit for PyTorch development.

[IJCAI 2021] Cross-Domain Few-Shot Classification via Adversarial Task Augmentation

"Learning to learn by gradient descent by gradient descent "by PyTorch -- a simple re-implementation.

Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.

The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].

Code for: "Learning To Adapt For Stereo" accepted at CVPR2019

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

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

My notes and assignment solutions for Stanford CS330 (Fall 2019 & 2020) Deep Multi-Task and Meta Learning

A collection of extensions and data-loaders for few-shot learning & 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.

PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models (IN PROGRESS)

[CVPR'17] Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to high performance at fast speed..

Memory efficient MAML using gradient checkpointing

Collection for Few-shot Learning

Learning Meta Face Recognition in Unseen Domains, CVPR, Oral, 2020

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

Meta learning with BERT as a learner

Awesome Multitask Learning Resources

The source codes of the paper "Improving Few-shot Text Classification via Pretrained Language Representations" and "When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification".

Implementation of "Make One-Shot Video Object Segmentation Efficient Again” and the semi-supervised fine-tuning "e-OSVOS" approach (NeurIPS 2020).

Combine multiple MNIST digits to create datasets with 100/1000 classes for few-shot learning/meta-learning

Meta graph convolutional neural network-assisted resilient swarm communications

Learning to Learn how to Learn: Self-Adaptive Visual Navigation using Meta-Learning (https://arxiv.org/abs/1812.00971)

DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020

Tensorflow Implementation of MAML

Personalizing Dialogue Agents via Meta-Learning

Paper Lists, Notes and Slides, Focus on NLP. For summarization, please refer to https://github.com/xcfcode/Summarization-Papers

Open-L2O: A Comprehensive and Reproducible Benchmark for Learning to Optimize Algorithms

Few-shot binary text classification with Induction Networks and Word2Vec weights initialization

Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)

pytorch implementation of Optimization as a Model for Few-shot Learning

This repository implements the paper, Model-Agnostic Meta-Leanring for Fast Adaptation of Deep Networks.

Meta-Feature Extractor

Source code for CVPR 2020 paper "Scene-Adaptive Video Frame Interpolation via Meta-Learning"

Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".

A resource list for causality in statistics, data science and physics

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

The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning"

Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning

[ICLR'19] Meta-learning with differentiable closed-form solvers

Hierarchical Co-occurrence Network with Prototype Loss for Few-shot Learning (PyTorch)