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 751–800 of 2964 papers

TitleStatusHype
Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code—0
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation—0
CoCoP: Enhancing Text Classification with LLM through Code Completion Prompt—0
A Simple Task-aware Contrastive Local Descriptor Selection Strategy for Few-shot Learning between inter class and intra class—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation—0
Towards Cross-Granularity Few-Shot Learning: Coarse-to-Fine Pseudo-Labeling with Visual-Semantic Meta-Embedding—0
A Similarity Paradigm Through Textual Regularization Without Forgetting—0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—0
Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning—0
Few-Shot Bayesian Optimization with Deep Kernel Surrogates—0
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Few-shot Anomaly Detection in Text with Deviation Learning—0
Affinity Network Fusion and Semi-supervised Learning for Cancer Patient Clustering—0
Few-Shot Authorship Attribution in English Reddit Posts—0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping—0
Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning—0
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors—0
A Broad Dataset is All You Need for One-Shot Object Detection—0
A Nested Bi-level Optimization Framework for Robust Few Shot Learning—0
A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification—0
Few-Shot Batch Incremental Road Object Detection via Detector Fusion—0
Few-shot Classification on Graphs with Structural Regularized GCNs—0
Few Shot Activity Recognition Using Variational Inference—0
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks—0
Few-Shot Adaptation for Multimedia Semantic Indexing—0
Clip4Retrofit: Enabling Real-Time Image Labeling on Edge Devices via Cross-Architecture CLIP Distillation—0
A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts—0
Few-Shot Adaptation of Grounding DINO for Agricultural Domain—0
Clinical Risk Prediction Using Language Models: Benefits And Considerations—0
AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations—0
Are Large Language Models Good Essay Graders?—0
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization—0
Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting—0
A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots—0
Are Few-shot Learning Benchmarks Too Simple ?—0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning—0
CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models—0
A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
Class-Specific Channel Attention for Few-Shot Learning—0
Class Interference Regularization—0
Class-Incremental Few-Shot Event Detection—0
Are Fewer Labels Possible for Few-shot Learning?—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
Few-Shot Adversarial Domain Adaptation—0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
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All datasetsMedConceptsQADTDFGVC-AircraftMini-ImageNet - 5-Shot LearningMini-Imagenet 5-way (1-shot)Stanford CarsMini-ImageNet - 1-Shot LearningPubMedQACaltech101CaseHOLDEuroSATFlowers-102

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