https://github.com/JeonMinkyu/MAML_Pytorch
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Papers with Code 아카이브
Few-Shot Image Classification
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Few-Shot Image Classification is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically < 6 examples). The goal is to enable models to recognize and classify new images with minimal supervision and limited data, without having to train on large datasets. (typically < 6 examples)
<span style="color:grey; opacity: 0.6">( Image credit: Learning Embedding Adaptation for Few-Shot Learning )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Jo-dsa/SemanticSeg
https://github.com/JoegameZhou/mPanGu-Alpha-53
https://github.com/JudyYe/zero-shot-gcn
Zero-Shot Learning by Convex Combination of Semantic Embeddings
https://github.com/KamalM8/Few-Shot-learning-Fashion
https://github.com/Kaushalya/medclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/KingBoyBIT/yolov3test
https://github.com/LIU1514/Yolov3-
https://github.com/LKLQQ/LEO
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Lieberk/Paddle-FSL-Baseline
https://github.com/Lieberk/Paddle-FSL-GNN
https://github.com/Lieberk/Paddle-MatchingNet
https://github.com/LiuXinyu12378/MatchingNetworks
https://github.com/Liuhong99/Imbalanced-SSL
Self-supervised Learning is More Robust to Dataset Imbalance
https://github.com/Lmath11/darknet
https://github.com/LourisXu/rfs
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?
https://github.com/MIC-DKFZ/DetectionAndRegression
https://github.com/MIC-DKFZ/RegRCNN
https://github.com/ML-Bee/associative-alignment-fs
https://github.com/MariefW/6.-trainingYOLO
https://github.com/Maskify/darknet
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/On_First_Order_Meta_Learning_Algorithms
https://github.com/Maymaher/StackGANv2
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/MegEngine/Models/tree/master/official/vision/detection
https://github.com/Megvii-Nanjing/ML_GCN
Multi-Label Image Recognition with Graph Convolutional Networks
https://github.com/Michedev/Prototypical-Networks
https://github.com/Michedev/snail
https://github.com/Mind23-2/MindCode-101/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-176
https://github.com/Mind23-2/MindCode-3/tree/main/MAML
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-5/tree/main/ProtoNet
https://github.com/Mind23-2/MindCode-55
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Mind23-2/MindCode-66
https://github.com/MindCode-4/code-11/tree/main/Class-balanced-loss-pytorch-master
https://github.com/MindCode-4/code-11/tree/main/comparing-the-efficacy
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.