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

Showing 10011050 of 2964 papers

TitleStatusHype
Overthinking the Truth: Understanding how Language Models Process False DemonstrationsCode1
Towards Task Sampler Learning for Meta-LearningCode1
Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute ManipulationCode1
TinyMetaFed: Efficient Federated Meta-Learning for TinyML0
A metric learning approach for endoscopic kidney stone identification0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification0
SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation0
Text Descriptions are Compressive and Invariant Representations for Visual Learning0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?0
Proto-CLIP: Vision-Language Prototypical Network for Few-Shot LearningCode1
Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?0
Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action RecognitionCode0
Diverse Retrieval-Augmented In-Context Learning for Dialogue State TrackingCode0
TablEye: Seeing small Tables through the Lens of Images0
On Conditional and Compositional Language Model Differentiable Prompting0
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation0
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
Meta-training with Demonstration Retrieval for Efficient Few-shot Learning0
Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic SegmentationCode1
Benchmarking Large Language Model Capabilities for Conditional Generation0
Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection0
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided DiffusionCode1
Language models are weak learners0
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial PerturbationsCode1
Is Pre-training Truly Better Than Meta-Learning?0
Mutually Guided Few-shot Learning for Relational Triple ExtractionCode0
FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair0
NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNNCode0
Visually grounded few-shot word learning in low-resource settings0
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts0
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language UnderstandingCode0
Multilingual Few-Shot Learning via Language Model Retrieval0
Channel-Spatial-Based Few-Shot Bird Sound Event Detection0
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
Federated Few-shot LearningCode1
FewSAR: A Few-shot SAR Image Classification BenchmarkCode1
Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models0
Few-shot bioacoustic event detection at the DCASE 2023 challengeCode1
Neural Fine-Tuning Search for Few-Shot LearningCode1
DocumentNet: Bridging the Data Gap in Document Pre-Training0
Inductive Linear Probing for Few-shot Node Classification0
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction0
FLamE: Few-shot Learning from Natural Language Explanations0
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis0
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuningCode2
Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning0
AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing0
EventCLIP: Adapting CLIP for Event-based Object RecognitionCode1
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image UnderstandingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91Unverified
2gpt-4-0125-previewAccuracy52.49Unverified
3gpt-3.5-turboAccuracy41.48Unverified
4gpt-3.5-turboAccuracy37.06Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63Unverified
6yikuan8/Clinical-LongformerAccuracy25.55Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06Unverified
8yikuan8/Clinical-LongformerAccuracy25.04Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27Unverified
2SaSPA + CAL4-shot Accuracy48.3Unverified
3Real-Guidance + CAL4-shot Accuracy41.5Unverified
4CAL4-shot Accuracy40.9Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2Unverified
2CALHarmonic mean35.2Unverified
3Variational Prompt TuningHarmonic mean34.69Unverified
4Real-Guidance + CALHarmonic mean34.5Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7Unverified
2TIM-GDAccuracy87.4Unverified
3UNEM-GaussianAccuracy66.4Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75Unverified
2HCTransformers5 way 1~2 shot74.74Unverified
3HyperShotAccuracy53.18Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7Unverified
2Real-Guidance + CAL4-shot Accuracy44.3Unverified
3CAL4-shot Accuracy42.2Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74Unverified
2DPGNAcc67.6Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79Unverified