104 Open source projects that are alternatives of or similar to Maxl

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

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

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

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

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

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

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

Tensorflow Implementation of MAML

This is an official implementation for "Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising"(KDD2021).

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

Meta graph convolutional neural network-assisted resilient swarm communications

Repository to track the progress in Meta-Learning (MtL), including the datasets and the current state-of-the-art for the most common MtL problems.

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

Personalizing Dialogue Agents via Meta-Learning

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

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

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.

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

Memory efficient MAML using gradient checkpointing

Code for meta-learning initializations for image segmentation

Collection for Few-shot Learning

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

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

[CVPR2021] Meta Batch-Instance Normalization for Generalizable Person Re-Identification

Awesome Multitask Learning Resources

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

Tensorflow Implementation of FaceNet: A Unified Embedding for Face Recognition and Clustering to find the celebrity whose face matches the closest to yours.

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

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

Graph meta learning via local subgraphs (NeurIPS 2020)

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

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

No description or website provided.

A PyTorch Library for Meta-learning Research

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

Repository for few-shot learning machine learning projects

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

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

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

Automated Machine Learning with scikit-learn

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

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

MetaGenRL, a novel meta reinforcement learning algorithm. Unlike prior work, MetaGenRL can generalize to new environments that are entirely different from those used for meta-training.

A dataset of datasets for learning to learn from few examples

Meta-DETR: Official PyTorch Implementation

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

Pytorch implementation of Meta-Learning for Short Utterance Speaker Recognition with Imbalance Length Pairs (Interspeech, 2020)

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 implementation for paper: Learning to Compare: Relation Network for Few-Shot Learning.

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

Meta-Learning through Hebbian Plasticity in Random Networks: https://arxiv.org/abs/2007.02686

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

Python Meta-Feature Extractor package.

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

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

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

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

Meta learning with BERT as a learner

Meta-Feature Extractor

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