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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/whut-yirong/espt

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ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning

https://github.com/winycg/MCL-OKD

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Multi-view Contrastive Learning for Online Knowledge Distillation

https://github.com/winycg/mcl

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Mutual Contrastive Learning for Visual Representation Learning

https://github.com/wjdghks950/discern-and-answer

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Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise

https://github.com/wltjr1007/FDEN

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A Plug-in Method for Representation Factorization in Connectionist Models

https://github.com/wshi83/ehragent

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EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

https://github.com/wssun/promptcs

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A Prompt Learning Framework for Source Code Summarization

https://github.com/wxie2013/gdfree-supervised-snn-mnist

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Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition

https://github.com/wy1iu/NSL

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Neural Similarity Learning

https://github.com/wyharveychen/CloserLookFewShot

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A Closer Look at Few-shot Classification

https://github.com/xiangyu8/PT-MAP-sf

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Few-Shot Learning by Integrating Spatial and Frequency Representation

https://github.com/xianyongqin/few-shot-video-classification

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Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation

https://github.com/xiaomengyc/Few-Shot-Semantic-Segmentation-Papers

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Objectness-Aware Few-Shot Semantic Segmentation

https://github.com/xiaomengyc/SG-One

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SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation

https://github.com/xiaoshuihuang/epcl

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EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder

https://github.com/xin-wang18/MISE

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MISE: Meta-knowledge Inheritance for Social Media-Based Stressor Estimation

https://github.com/xinyadu/rgqa

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Retrieval-Augmented Generative Question Answering for Event Argument Extraction

https://github.com/xiongxiaoxu/metagad

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MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection

https://github.com/xiye17/interpcalib

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Can Explanations Be Useful for Calibrating Black Box Models?

https://github.com/xlhex/gal_syntex

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Generate, Annotate, and Learn: NLP with Synthetic Text

https://github.com/xmed-lab/scan

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Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification

https://github.com/xuechaochen/p3p-mae

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P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked Autoencoders

https://github.com/xuesongwang/global-convolutional-neural-processes

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Global Convolutional Neural Processes

https://github.com/xuhuali-mxj/im-dcl

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Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning

https://github.com/xuhuali-mxj/stepspt

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Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning

https://github.com/xulabs/aitom

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Training-free CryoET Tomogram Segmentation

https://github.com/xxlifelover/meta-gradient-augmentation

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Improving Generalization in Meta-Learning via Meta-Gradient Augmentation

https://github.com/xxyqwq/metascript

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MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networks

https://github.com/y1ny/WordDeletion

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Active Use of Latent Constituency Representation in both Humans and Large Language Models

https://github.com/yan-scnu/prompted_dynamic_detection

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Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4

https://github.com/yananwu0510/ml-sgm

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Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition

https://github.com/yang-yk/NP-RepMet

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Restoring Negative Information in Few-Shot Object Detection

https://github.com/yangarbiter/nearest-category-generalization

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Probing Predictions on OOD Images via Nearest Categories

https://github.com/yangpuhai/Granularity-in-DST

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Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?

https://github.com/yangxu351/synthetic_xview_airplanes

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SIMPL: Generating Synthetic Overhead Imagery to Address Zero-shot and Few-Shot Detection Problems

https://github.com/yangyangyang127/ape

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Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement

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