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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/chenhaoxing/etl4video

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Efficient Transfer Learning for Video-language Foundation Models

https://github.com/chenhaoxing/ssformers

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Sparse Spatial Transformers for Few-Shot Learning

https://github.com/chenjiayi68/dmtnet

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Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation

https://github.com/chicagohai/hans-explanations

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Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI

https://github.com/chiyuzhang94/pmlm-sft

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Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning

https://github.com/chris210634/word_soups

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Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning

https://github.com/christopher-beckham/challenges-few-shot-gans

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Overcoming challenges in leveraging GANs for few-shot data augmentation

https://github.com/christoschr97/misinf-detection-llms

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Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models

https://github.com/chrisyxue/RCN_for_Interpretable_few_shot

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Region Comparison Network for Interpretable Few-shot Image Classification

https://github.com/chu-data-lab/GOGGLES

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GOGGLES: Automatic Image Labeling with Affinity Coding

https://github.com/chunbolang/BAM

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Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

https://github.com/chunbolang/DCP

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Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation

https://github.com/chuxuzhang/AAAI2020_FSRL

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Few-Shot Knowledge Graph Completion

https://github.com/cjfarmer/trd_fsl

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Pre-trained Token-replaced Detection Model as Few-shot Learner

https://github.com/cl-victor1/exalt_2024_bcsz

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TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection

https://github.com/claws-lab/few-shot-adversarial-robustness

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Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding

https://github.com/clcl-geneva/blm-snfdisentangling

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Grammatical information in BERT sentence embeddings as two-dimensional arrays

https://github.com/cleopatra-itn/sentimentanalyserslhrnews

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Multi-task Learning for Cross-Lingual Sentiment Analysis

https://github.com/cliffbb/oem-fewshot-challenge

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Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark

https://github.com/clovaai/wsolevaluation

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Evaluating Weakly Supervised Object Localization Methods Right

https://github.com/clp-research/language-models-multimodal-tasks

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Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks

https://github.com/clulab/releases

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From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction

https://github.com/cmu-plus/lak2025-inequity

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Do Tutors Learn from Equity Training and Can Generative AI Assess It?

https://github.com/cnguyen10/few_shot_meta_learning

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Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

https://github.com/cnguyen10/similarity_classification_tasks

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Similarity of Classification Tasks

https://github.com/cofly2014/tsa-mlt

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Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition

https://github.com/cogentlabs/apl

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Adaptive Posterior Learning: few-shot learning with a surprise-based memory module

https://github.com/cogsys-tudelft/chameleon

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Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data

https://github.com/colinzhaoust/intrinsic_fewshot_hardness

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On Measuring the Intrinsic Few-Shot Hardness of Datasets

https://github.com/complex-ai-lab/ncc

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Neural Conformal Control for Time Series Forecasting

https://github.com/composable-models/llm_multiagent_debate

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Improving Factuality and Reasoning in Language Models through Multiagent Debate

https://github.com/computer-vision-in-the-wild/cvinw_readings

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Vision-Language Pre-training: Basics, Recent Advances, and Future Trends

https://github.com/cooelf/OneshotQA

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One-shot Learning for Question-Answering in Gaokao History Challenge

https://github.com/copenlu/scientific-exaggeration-detection

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Semi-Supervised Exaggeration Detection of Health Science Press Releases

https://github.com/corne00/ddunetforheatplumeprediction

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Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport

https://github.com/cos4007/grape_demonstrations

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Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension

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