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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/xvyaward/owq

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xxxnhb/few-shot-gnn

Few-Shot Learning with Graph Neural Networks

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

Edge-labeling Graph Neural Network for Few-shot Learning

https://github.com/xzhang97666/alpacare

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yangalan123/amortized-interpretability

Can Explanations Be Useful for Calibrating Black Box Models?

https://github.com/yangcedrus/ProtoNet

Prototypical Networks for Few-shot Learning

https://github.com/yangfeifei02/HOSP

High-order structure preserving graph neural network for few-shot learning

https://github.com/yangyangyang127/tfs3d

No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation

https://github.com/yangyucheng000/MSpaper/tree/main/unisiam

Self-Supervision Can Be a Good Few-Shot Learner

https://github.com/yangyucheng000/University/tree/main/model-3/seggpt

SegGPT: Segmenting Everything In Context

https://github.com/yaoyao-liu/mini-imagenet-tools

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/yi-han/BERT_Relation_Extraction

Matching the Blanks: Distributional Similarity for Relation Learning

https://github.com/yijiuzai/Matching-Networks-for-One-Shot-Learning

Matching Networks for One Shot Learning

https://github.com/yining610/ProtSi-Network

ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer Evaluation

https://github.com/yiren-jian/embedding-learning-fsl

Self-supervised Knowledge Distillation for Few-shot Learning

https://github.com/yjt2018/fewshot-egnn

Edge-labeling Graph Neural Network for Few-shot Learning

https://github.com/yongchao98/promst

Large Language Models Are Human-Level Prompt Engineers

https://github.com/yoonholee/MT-net

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/young-geng/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yredwood/fewshot_blogpost

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/yuanmu97/secure-transformer-inference

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yuhao318/atomic_feature_mimicking

Reviving Undersampling for Long-Tailed Learning

https://github.com/yuxiaw/openfactcheck

RARR: Researching and Revising What Language Models Say, Using Language Models

https://github.com/zach-zhiling-zheng/reticular_chemist

GPT-4 Technical Report

https://github.com/zain-jiang/speech-editing-toolkit

CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing

https://github.com/zhanglab-aim/lingo

The Power of Scale for Parameter-Efficient Prompt Tuning

https://github.com/zhangxingcheng/-

Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study

https://github.com/zhaohan-xi/plm-prompt-defense

Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

https://github.com/zhmiao/OpenLongTailRecognition-OLTR

Large-Scale Long-Tailed Recognition in an Open World

https://github.com/zhongwanjun/AR-LSAT

From LSAT: The Progress and Challenges of Complex Reasoning

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

Diverse Few-Shot Text Classification with Multiple Metrics

https://github.com/zihanwangki/x-tc

Prototypical Calibration for Few-shot Learning of Language Models

https://github.com/zihanzhaosjtu/librisqa

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ziqihuangg/collaborative-diffusion

ReVersion: Diffusion-Based Relation Inversion from Images

https://github.com/zixuanke/pycontinual

Continual Training of Language Models for Few-Shot Learning

https://github.com/zjlab-ammi/q-net

Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation

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