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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/anfeather/eigen-reptile

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Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile

https://github.com/angtian/neuralvs

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Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose

https://github.com/angusmaiden/complex-fer

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Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features

https://github.com/anish-lu-yihe/pySVRT

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Program synthesis performance constrained by non-linear spatial relations in Synthetic Visual Reasoning Test

https://github.com/annaproxy/udify-metalearning

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Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing

https://github.com/annusha/lcwof

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Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting

https://github.com/anonymous4545/jers

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One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data

https://github.com/anonymouscode-computerscience/ride

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RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration Exemplars

https://github.com/anthonywchen/rarr

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

https://github.com/antonpuz/DeROL

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Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems

https://github.com/anugunjnaman/fixed-maml

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Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion Recognition

https://github.com/anujinho/trident

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Transductive Decoupled Variational Inference for Few-Shot Classification

https://github.com/apple2373/MetaIRNet

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Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition

https://github.com/applicaai/contract-discovery

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Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines

https://github.com/aravindr93/imaml_dev

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Meta-Learning with Implicit Gradients

https://github.com/arjish/PreTrainedFullLibrary_FewShot

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Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier

https://github.com/arjish/meta-meta-classification

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Meta-Meta Classification for One-Shot Learning

https://github.com/arnab39/FewShot_GAN-Unet3D

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Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

https://github.com/arnav0400/vit-slim

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One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

https://github.com/ars22/meta-learning-eval-id-vs-ood

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Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution

https://github.com/ashaw596/meta_architecture_search

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Meta Architecture Search

https://github.com/ashengl/fsncd

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Few-shot Novel Category Discovery

https://github.com/ashiq24/coda-no

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Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs

https://github.com/ashwinpathak20/idiomaticity_detection_using_few_shot_learning

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JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning

https://github.com/asrafulashiq/dynamic-cdfsl

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Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data

https://github.com/atapour/ransomware-classification

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A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation

https://github.com/atik666/representationtransfer

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Unsupervised Representation Learning to Aid Semi-Supervised Meta Learning

https://github.com/atmahou/promptslottagging

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Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging

https://github.com/atulkum/pointer_summarizer

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Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View

https://github.com/auniquesun/ppt

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Parameter-efficient Prompt Learning for 3D Point Cloud Understanding

https://github.com/avm-debatr/bagganhq

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Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images

https://github.com/avm-debatr/ganecdotes

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Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images

https://github.com/avyavkumar/few-shot-learning-notebooks

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Learning New Tasks from a Few Examples with Soft-Label Prototypes

https://github.com/awebson/prompt_semantics

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Do Prompt-Based Models Really Understand the Meaning of their Prompts?

https://github.com/azizanlab/unlimitd

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Uncertainty-Aware Meta-Learning for Multimodal Task Distributions

https://github.com/baaivision/painter

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SegGPT: Segmenting Everything In Context

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