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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/tiangeluo/fsl-global

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Few-Shot Learning with Global Class Representations

https://github.com/tianjunz/tempera

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TEMPERA: Test-Time Prompting via Reinforcement Learning

https://github.com/tibhannover/patent-figure-classification

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Patent Figure Classification using Large Vision-language Models

https://github.com/tinkez/fap_cdfsc

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Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting

https://github.com/tinyaway/manet

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Few-shot semantic segmentation via mask aggregation

https://github.com/tju-sjyj/amu-tuning

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AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning

https://github.com/tmlr-group/CoPA

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Mind the Gap Between Prototypes and Images in Cross-domain Finetuning

https://github.com/tmlr-group/mokd

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MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence

https://github.com/tmlr-group/wca

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Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models

https://github.com/tntek/dspnet

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Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

https://github.com/tomte812/chef

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Cross-Domain Few-Shot Learning by Representation Fusion

https://github.com/tonysy/PyAction

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Learning Implicit Temporal Alignment for Few-shot Video Classification

https://github.com/tonyzhaozh/few-shot-learning

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Calibrate Before Use: Improving Few-Shot Performance of Language Models

https://github.com/tricktreat/promptner

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PromptNER: Prompt Locating and Typing for Named Entity Recognition

https://github.com/tsinghua-fib-lab/gpd

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Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation

https://github.com/tsinghuac3i/llm4biohypogen

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Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation

https://github.com/tuantuange/dhm-uht

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Unsupervised Meta-Learning via Dynamic Head and Heterogeneous Task Construction for Few-Shot Classification

https://github.com/tunib-ai/parallelformers

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

https://github.com/twelfth-star/LinkGPT

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LinkGPT: Teaching Large Language Models To Predict Missing Links

https://github.com/txsun1997/black-box-tuning

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BBTv2: Towards a Gradient-Free Future with Large Language Models

https://github.com/tyhuang0428/CLIP2Point

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CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training

https://github.com/tylersco/adapted_deep_embeddings

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Adapted Deep Embeddings: A Synthesis of Methods for $k$-Shot Inductive Transfer Learning

https://github.com/typilus/typilus

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Typilus: Neural Type Hints

https://github.com/ubc-nlp/sparrow

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The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages

https://github.com/uber/ludwig

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Ludwig: a type-based declarative deep learning toolbox

https://github.com/ubiquitouslearning/fes

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Federated Few-Shot Learning for Mobile NLP

https://github.com/ucbdrive/tafe-net

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TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning

https://github.com/uci-cbcl/RP-Net

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Recurrent Mask Refinement for Few-Shot Medical Image Segmentation

https://github.com/ucinlp/null-prompts

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Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models

https://github.com/uclanlp/paraamr

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ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation

https://github.com/udelaqui/foundation_models_for_polyp_detection_segmentation

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AI-Assisted Colonoscopy: Polyp Detection and Segmentation using Foundation Models

https://github.com/uic-liu-lab/cpt

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Continual Training of Language Models for Few-Shot Learning

https://github.com/uitml/nohub

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Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings

https://github.com/umbertocappellazzo/petl_ast

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Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers

https://github.com/uncbiag/easyreg

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Anatomical Data Augmentation via Fluid-based Image Registration

https://github.com/uobinxiao/semantic_cross_attention_fsl

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Semantic Cross Attention for Few-shot Learning

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