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Few-Shot Learning
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Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.
Source: Penalty Method for Inversion-Free Deep Bilevel Optimization
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fsod/cd-fsod
공식 코드CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detection
https://github.com/fukexue/fast
공식 코드FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification
https://github.com/fukexue/mcp-bert
공식 코드https://github.com/gabriel-skantze/CoLLIE
공식 코드CoLLIE: Continual Learning of Language Grounding from Language-Image Embeddings
https://github.com/ganatra-v/gaussian-sampling-fsl
공식 코드Logarithm-transform aided Gaussian Sampling for Few-Shot Learning
https://github.com/gaoren002/Anomaly-multiclassification
공식 코드Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task
https://github.com/gaoxiangluo/llm-biomed-ner-er
공식 코드An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing
https://github.com/gasiki/eeg-reptile
공식 코드EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIs
https://github.com/gchochla/z2fsl
공식 코드End-to-end Generative Zero-shot Learning via Few-shot Learning
https://github.com/georgevern/lmcor
공식 코드Small Language Models Improve Giants by Rewriting Their Outputs
https://github.com/georgiarg/autofashion
공식 코드Automatic Generation of Fashion Images using Prompting in Generative Machine Learning Models
https://github.com/georgosgeorgos/hierarchical-few-shot-generative-models
공식 코드SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
https://github.com/ggjy/vision_weak_to_strong
공식 코드Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
https://github.com/gguibon/protoseq
공식 코드Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks
https://github.com/ghazalmilani/one-shot-learning-from-text-iclp2023
공식 코드Towards One-Shot Learning for Text Classification using Inductive Logic Programming
https://github.com/gianlucarloni/causality_conv_nets
공식 코드Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results
https://github.com/gidariss/FewShotWithoutForgetting
공식 코드https://github.com/gidariss/wDAE_GNN_FewShot
공식 코드Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning
https://github.com/giilde/ft-vs-icl
공식 코드https://github.com/gingl/cmpa
공식 코드https://github.com/gitgyun/visual_token_matching
공식 코드Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching
https://github.com/githubnext/testpilot
공식 코드An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation
https://github.com/gjiaying/keaf
공식 코드Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction
https://github.com/gjiaying/zslrc
공식 코드https://github.com/gmum/few-shot-hypernets-public
공식 코드https://github.com/gmum/hyperplanes
공식 코드https://github.com/gmum/non-gaussian-gaussian-processes
공식 코드https://github.com/goldblum/AdversarialQuerying
공식 코드Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
https://github.com/google-research-datasets/qameleon
공식 코드https://github.com/google-research/big_transfer
공식 코드https://github.com/google-research/big_vision
공식 코드https://github.com/google-research/cascades
공식 코드https://github.com/google-research/google-research/tree/master/STraTA
공식 코드STraTA: Self-Training with Task Augmentation for Better Few-shot Learning
https://github.com/google-research/google-research/tree/master/hypertransformer
공식 코드HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
https://github.com/google-research/meta-dataset
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.