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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/kaipengm2/DiffKendall

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DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation

https://github.com/kaist-lklab/cot-collection

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The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

https://github.com/kaistai/cot-collection

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The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

https://github.com/kaixin96/PANet

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PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment

https://github.com/kaize0409/GPN

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Graph Prototypical Networks for Few-shot Learning on Attributed Networks

https://github.com/kaize0409/Meta-GDN_AnomalyDetection

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Few-shot Network Anomaly Detection via Cross-network Meta-learning

https://github.com/kalrfou/swint-pretrained-microscopy-models

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Transfer Learning for Microstructure Segmentation with CS-UNet: A Hybrid Algorithm with Transformer and CNN Encoders

https://github.com/kargaranamir/MenuCraft

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MenuCraft: Interactive Menu System Design with Large Language Models

https://github.com/karpurapus/bddgpt-automate-tests

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Comprehensive Evaluation and Insights into the Use of Large Language Models in the Automation of Behavior-Driven Development Acceptance Test Formulation

https://github.com/karthik-d/few-shot-learning-skin-analysis

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Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative Embeddings

https://github.com/kartikgill/easter2

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Easter2.0: Improving convolutional models for handwritten text recognition

https://github.com/katbailey/few-shot-text-classification

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Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop

https://github.com/kate-sann5100/SS_few_shot

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Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes

https://github.com/kate-sann5100/crossinstitutionfewshotsegmentation

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Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

https://github.com/katsumiok/askit-artifact

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AskIt: Unified Programming Interface for Programming with Large Language Models

https://github.com/katsumiok/pyaskit

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AskIt: Unified Programming Interface for Programming with Large Language Models

https://github.com/katsumiok/ts-askit

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AskIt: Unified Programming Interface for Programming with Large Language Models

https://github.com/kavehhassani/metagrl

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Cross-Domain Few-Shot Graph Classification

https://github.com/kclip/bayesian_active_meta_learning

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Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization

https://github.com/kddubey/pretrain-on-test

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Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classification

https://github.com/kding1225/softcpt

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Prompt Tuning with Soft Context Sharing for Vision-Language Models

https://github.com/keaml-jlu/star

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Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport

https://github.com/keanson/md-bsfc

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Accelerating Convergence in Bayesian Few-Shot Classification

https://github.com/keeplearning-again/matchseg

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MatchSeg: Towards Better Segmentation via Reference Image Matching

https://github.com/keirp/automatic_prompt_engineer

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Large Language Models Are Human-Level Prompt Engineers

https://github.com/kelichiu/GPT3-hate-speech-detection

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Detecting Hate Speech with GPT-3

https://github.com/kent0n-li/medical-image-segmentation

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nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance

https://github.com/kevinma1515/gpt_idetc

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Conceptual Design Generation Using Large Language Models

https://github.com/kevinsrr/repal

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Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction

https://github.com/kexin-kxzhang/optfusion

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Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models

https://github.com/khalil-research/arga-aaai23

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Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus

https://github.com/khy0809/fewshot-egnn

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Edge-labeling Graph Neural Network for Few-shot Learning

https://github.com/kieranjwood/x-trend

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Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies

https://github.com/kikimormay/fsl-tcbr

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Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid

https://github.com/kilimanj4r0/code-summarization-beyond-function-level

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Code Summarization Beyond Function Level

https://github.com/kinit-sk/selec-strats-for-aug

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Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.