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 201250 of 2964 papers

TitleStatusHype
Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real WorldCode1
In-Context Learning with Iterative Demonstration SelectionCode1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series PretrainingCode1
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-trainingCode1
Recurrent Hypernetworks are Surprisingly Strong in Meta-RLCode1
Domain Adaptive Few-Shot Open-Set LearningCode1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient ChannelsCode1
GLAD: Content-aware Dynamic Graphs For Log Anomaly DetectionCode1
Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic SegmentationCode1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical TransformerCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive LearningCode1
Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-LearningCode1
AskIt: Unified Programming Interface for Programming with Large Language ModelsCode1
Read-only Prompt Optimization for Vision-Language Few-shot LearningCode1
Transfer Learning for Microstructure Segmentation with CS-UNet: A Hybrid Algorithm with Transformer and CNN EncodersCode1
Diagnosing Infeasible Optimization Problems Using Large Language ModelsCode1
Link-Context Learning for Multimodal LLMsCode1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
LLMeBench: A Flexible Framework for Accelerating LLMs BenchmarkingCode1
Learning Multi-modal Representations by Watching Hundreds of Surgical Video LecturesCode1
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproachCode1
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
Proto-CLIP: Vision-Language Prototypical Network for Few-Shot LearningCode1
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic SegmentationCode1
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided DiffusionCode1
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial PerturbationsCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
Federated Few-shot LearningCode1
FewSAR: A Few-shot SAR Image Classification BenchmarkCode1
Neural Fine-Tuning Search for Few-Shot LearningCode1
Few-shot bioacoustic event detection at the DCASE 2023 challengeCode1
EventCLIP: Adapting CLIP for Event-based Object RecognitionCode1
Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting SystemsCode1
Consistency-guided Prompt Learning for Vision-Language ModelsCode1
Task-Equivariant Graph Few-shot LearningCode1
Deeply Coupled Cross-Modal Prompt LearningCode1
The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image ClassificationCode1
Learning to Learn from APIs: Black-Box Data-Free Meta-LearningCode1
Training on Thin Air: Improve Image Classification with Generated DataCode1
Sentiment Analysis in the Era of Large Language Models: A Reality CheckCode1
Improving few-shot learning-based protein engineering with evolutionary samplingCode1
Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?Code1
Small Language Models Improve Giants by Rewriting Their OutputsCode1
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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