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Few-Shot Learning

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하위 태스크: 12
평가표: 27
보존된 평가 기록: 52

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/njulus/ST

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Towards Enabling Meta-Learning from Target Models

https://github.com/njulus/gkd

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Generalized Knowledge Distillation via Relationship Matching

https://github.com/nle18/coref-llms

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Are Large Language Models Robust Coreference Resolvers?

https://github.com/nmank/FD

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A Flag Decomposition for Hierarchical Datasets

https://github.com/nmi-lab/snn_maml

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Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing

https://github.com/nobody-1617/deta

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DETA: Denoised Task Adaptation for Few-Shot Learning

https://github.com/nraghuraman/bongard-context

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Support-Set Context Matters for Bongard Problems

https://github.com/numenta/htmpapers/tree/master/bmvc/grid_cell_path_integration_for_movement_based_visual_object_recognition

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Grid Cell Path Integration For Movement-Based Visual Object Recognition

https://github.com/nupurkmr9/S2M2_fewshot

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Charting the Right Manifold: Manifold Mixup for Few-shot Learning

https://github.com/nust-machine-intelligence-laboratory/fecanet

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FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network

https://github.com/nvakhoa/superclass-fsis

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Instance-level Few-shot Learning with Class Hierarchy Mining

https://github.com/nvlabs/bongard-hoi

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Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions

https://github.com/nvlabs/prismer

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Prismer: A Vision-Language Model with Multi-Task Experts

https://github.com/nyu-mlda/openabc

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Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis

https://github.com/nz0001na/mad_maf

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Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting

https://github.com/observeai-research/nli-fsl

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Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection

https://github.com/odabashi/LMs-in-News-Summarization

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Unraveling the Capabilities of Language Models in News Summarization

https://github.com/odegeasslbc/FastGAN-pytorch

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Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image Recognition

https://github.com/oezyurty/replm

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Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models

https://github.com/ojss/samptransfer

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Self-Attention Message Passing for Contrastive Few-Shot Learning

https://github.com/ondrejbohdal/evograd

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EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

https://github.com/oniani/llms-vote

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Large Language Models Vote: Prompting for Rare Disease Identification

https://github.com/open-debin/bayesian_mqda

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Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

https://github.com/openai/evals

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GPT-4 Technical Report

https://github.com/openai/gpt-3

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Language Models are Few-Shot Learners

https://github.com/openai/supervised-reptile

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On First-Order Meta-Learning Algorithms

https://github.com/oshapio/Efficient-Cross-Validation-of-Echo-State-Networks

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Efficient Cross-Validation of Echo State Networks

https://github.com/oughtinc/raft-baselines

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RAFT: A Real-World Few-Shot Text Classification Benchmark

https://github.com/ouhenio/llms-overstimate-profoundness

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Large Language Models are biased to overestimate profoundness

https://github.com/ouyangtianran/plml

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Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning

https://github.com/oveilleux/realistic_transductive_few_shot

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Realistic Evaluation of Transductive Few-Shot Learning

https://github.com/p-lambda/incontext-learning

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An Explanation of In-context Learning as Implicit Bayesian Inference

https://github.com/paint4poem/paint4poem

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Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems

https://github.com/pandeydeep9/units-ml-cvpr-22

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Multidimensional Belief Quantification for Label-Efficient Meta-Learning

https://github.com/parksandrecfan/VIRL

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VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimation

https://github.com/pasqualedem/LabelAnything

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Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts

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