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

Python Meta-Feature Extractor package.

PyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"

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

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

Awesome Few-shot learning

No description or website provided.

[ECCV 2020] Scale Adaptive Network: Learning to Learn Parameterized Classification Networks for Scalable Input Images

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

Code for FOCAL Paper Published at ICLR 2021

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

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

Implementation of our paper "Meta Reinforcement Learning with Task Embedding and Shared Policy"

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

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

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

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

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

Collection of Reinforcement Learning / Meta Reinforcement Learning Environments.

A dataset of datasets for learning to learn from few examples

Source code for ECCV2020 "Fast Adaptation to Super-Resolution Networks via Meta-Learning"

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

awesome few shot / meta learning papers

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

TensorFlow 2.0 implementation of MAML.

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.

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

Code for "One-Shot Visual Imitation Learning via Meta-Learning"

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

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

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

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

Personalizing Dialogue Agents via Meta-Learning

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

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

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.

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

Official implementation of Meta-StyleSpeech and StyleSpeech

Awesome Multitask Learning Resources

One-Shot Learning with Triplet CNNs in Pytorch

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

Code of Empirical Bayes Transductive Meta-Learning with Synthetic Gradients

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 codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (Neural Networks 2022 - in submission)

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

Implementation for <Neural Similarity Learning> in NeurIPS'19.

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

Learning To Compare For Text , Few shot learning in text classification

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

Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)

Collection for Few-shot Learning

Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem

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

LibFewShot: A Comprehensive Library for Few-shot Learning.

Meta graph convolutional neural network-assisted resilient swarm communications

Code for meta-learning initializations for image segmentation

Repository for few-shot learning machine learning projects

Automated Machine Learning with scikit-learn

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

Meta-DETR: Official PyTorch Implementation