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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/ennengyang/awesome-model-merging-methods-theories-applications
공식 코드Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
https://github.com/epfl-nlp/scesc-llm-skill-extraction
공식 코드Rethinking Skill Extraction in the Job Market Domain using Large Language Models
https://github.com/epfllm/meditron
공식 코드MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
https://github.com/eric-ai-lab/pectvlm
공식 코드https://github.com/estelyang/qcircuitnet_dataset
공식 코드QCircuitNet: A Large-Scale Hierarchical Dataset for Quantum Algorithm Design
https://github.com/eternityyw/tram-benchmark
공식 코드TRAM: Benchmarking Temporal Reasoning for Large Language Models
https://github.com/ethanjperez/true_few_shot
공식 코드https://github.com/explainableml/avdiff-gfsl
공식 코드Text-to-feature diffusion for audio-visual few-shot learning
https://github.com/extensityai/benchmark
공식 코드SymbolicAI: A framework for logic-based approaches combining generative models and solvers
https://github.com/eyalmichaeli/saspa-aug
공식 코드Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
https://github.com/f3rm/f3rm
공식 코드Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation
https://github.com/facebookresearch/SentAugment
공식 코드Self-training Improves Pre-training for Natural Language Understanding
https://github.com/facebookresearch/atlas
공식 코드Atlas: Few-shot Learning with Retrieval Augmented Language Models
https://github.com/facebookresearch/electra-fewshot-learning
공식 코드Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models
https://github.com/facebookresearch/higher
공식 코드Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural Nets
https://github.com/facebookresearch/lawt
공식 코드https://github.com/facebookresearch/llama
공식 코드https://github.com/facebookresearch/metaicl
공식 코드https://github.com/facebookresearch/noisy_few_shot
공식 코드https://github.com/facebookresearch/perfect
공식 코드PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models
https://github.com/fangkaipeng/pros
공식 코드ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval
https://github.com/fanq15/FewX
공식 코드https://github.com/fanq15/ssp
공식 코드https://github.com/faresbougourzi/awesome-dl-for-medical-imaging-segmentation
공식 코드https://github.com/fatemehazimi990/RS2S
공식 코드Revisiting Sequence-to-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory
https://github.com/fatemehtd/Echo-SyncNet
공식 코드Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography
https://github.com/faziletgokbudak/One-shot-Photo-Retouching
공식 코드One-shot Detail Retouching with Patch Space Neural Transformation Blending
https://github.com/fcdl94/fss
공식 코드https://github.com/few-shot-learning-on-chest-x-ray/fsl_subspace
공식 코드Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random Discriminative Subspaces
https://github.com/firasl/BoCF
공식 코드https://github.com/fiveai/on-episodes-fsl
공식 코드https://github.com/flagopen/flagembedding
공식 코드https://github.com/flennerhag/warpgrad
공식 코드https://github.com/flitternie/graphq_ir
공식 코드https://github.com/frankzheng2022/prise
공식 코드PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.