SOTAVerified

Few-Shot Learning

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

Papers

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Showing 1951–2000 of 2964 papers

TitleStatusHype
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning—0
Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting—0
Clinical Risk Prediction Using Language Models: Benefits And Considerations—0
Clip4Retrofit: Enabling Real-Time Image Labeling on Edge Devices via Cross-Architecture CLIP Distillation—0
A Broad Dataset is All You Need for One-Shot Object Detection—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—0
Towards Cross-Granularity Few-Shot Learning: Coarse-to-Fine Pseudo-Labeling with Visual-Semantic Meta-Embedding—0
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation—0
CoCoP: Enhancing Text Classification with LLM through Code Completion Prompt—0
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation—0
Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code—0
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study—0
Collaboration of Pre-trained Models Makes Better Few-shot Learner—0
Combat Data Shift in Few-shot Learning with Knowledge Graph—0
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning—0
Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification—0
Compare learning: bi-attention network for few-shot learning—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification—0
Composing Diffusion Policies for Few-shot Learning of Movement Trajectories—0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing—0
Compositional Fine-Grained Low-Shot Learning—0
Compositional Generalization via Neural-Symbolic Stack Machines—0
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation—0
Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs—0
Compressor-Based Classification for Atrial Fibrillation Detection—0
Concept Discovery for Fast Adapatation—0
Conditional Neural Processes for Molecules—0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning—0
Configuration Validation with Large Language Models—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning—0
ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation—0
Conservative Generator, Progressive Discriminator: Coordination of Adversaries in Few-shot Incremental Image Synthesis—0
Consistency Analysis of ChatGPT—0
ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning—0
Constellation Loss: Improving the efficiency of deep metric learning loss functions for optimal embedding—0
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification—0
Context-Agnostic Learning Using Synthetic Data—0
Context-aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods—0
Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity—0
Contextual HyperNetworks for Novel Feature Adaptation—0
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery—0
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning—0
Continual Few-Shot Learning with Adversarial Class Storage—0
Continual Local Replacement for Few-shot Learning—0
Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning—0
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91—Unverified
2gpt-4-0125-previewAccuracy52.49—Unverified
3gpt-3.5-turboAccuracy41.48—Unverified
4gpt-3.5-turboAccuracy37.06—Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63—Unverified
6yikuan8/Clinical-LongformerAccuracy25.55—Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06—Unverified
8yikuan8/Clinical-LongformerAccuracy25.04—Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92—Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27—Unverified
2SaSPA + CAL4-shot Accuracy48.3—Unverified
3Real-Guidance + CAL4-shot Accuracy41.5—Unverified
4CAL4-shot Accuracy40.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2—Unverified
2CALHarmonic mean35.2—Unverified
3Variational Prompt TuningHarmonic mean34.69—Unverified
4Real-Guidance + CALHarmonic mean34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7—Unverified
2TIM-GDAccuracy87.4—Unverified
3UNEM-GaussianAccuracy66.4—Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75—Unverified
2HCTransformers5 way 1~2 shot74.74—Unverified
3HyperShotAccuracy53.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7—Unverified
2Real-Guidance + CAL4-shot Accuracy44.3—Unverified
3CAL4-shot Accuracy42.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74—Unverified
2DPGNAcc67.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9—Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57—Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79—Unverified