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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/aim-uofa/diffews

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Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation

https://github.com/aim-uofa/matcher

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Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

https://github.com/airi-institute/asi-certification

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Certification of Speaker Recognition Models to Additive Perturbations

https://github.com/ait-assistive-autonomous-systems/hopomop

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Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks

https://github.com/akariasai/attempt

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ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts

https://github.com/akoksal/meal

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MEAL: Stable and Active Learning for Few-Shot Prompting

https://github.com/albertqjiang/mma

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Multilingual Mathematical Autoformalization

https://github.com/aldrich2y/mianet

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MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

https://github.com/alessiabertugli/FUSION

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Few-Shot Unsupervised Continual Learning through Meta-Examples

https://github.com/alex-shilei/mshnet

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Multi-similarity based Hyperrelation Network for few-shot segmentation

https://github.com/alfassy/FETA

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FETA: Towards Specializing Foundation Models for Expert Task Applications

https://github.com/alibaba/EasyNLP

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EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing

https://github.com/alinlab/psco

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Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning

https://github.com/alisafaya/neurocache

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Neurocache: Efficient Vector Retrieval for Long-range Language Modeling

https://github.com/allenai/ethical-interventions

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Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?

https://github.com/allenai/flex

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FLEX: Unifying Evaluation for Few-Shot NLP

https://github.com/allenai/mmc4

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Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text

https://github.com/allenai/unifew

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FLEX: Unifying Evaluation for Few-Shot NLP

https://github.com/allenhaozhu/protolp

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Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement

https://github.com/alon-albalak/flad

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Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data

https://github.com/alrope123/z-icl

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Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations

https://github.com/amanpriyanshu/adaptkeybert

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AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERT

https://github.com/amazingren/point-cmae

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Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning

https://github.com/amazon-research/dse

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Learning Dialogue Representations from Consecutive Utterances

https://github.com/amazon-research/few-shot-object-detection-benchmark

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Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark

https://github.com/amazon-science/few-shot-baseline

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A Baseline for Few-Shot Image Classification

https://github.com/amazon-science/instruction-tuning-for-absa

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Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis

https://github.com/amazon-science/uniform-episodic-sampling

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Uniform Sampling over Episode Difficulty

https://github.com/amgao/rls-onlinetrack

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Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking

https://github.com/amiiir-sarfi/seal

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Simulated Annealing in Early Layers Leads to Better Generalization

https://github.com/amink8/folk

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Frequency-Guided Masking for Enhanced Vision Self-Supervised Learning

https://github.com/amirbar/detreg

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DETReg: Unsupervised Pretraining with Region Priors for Object Detection

https://github.com/amirrezafateh/msdnet

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MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping

https://github.com/anaryegen/few_shot_argument_mining

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Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques

https://github.com/andreamad8/FSB

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Few-Shot Bot: Prompt-Based Learning for Dialogue Systems

https://github.com/andrewbo29/mtm-meta-learning-sa

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Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning

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