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 14511500 of 2964 papers

TitleStatusHype
AniWho : A Quick and Accurate Way to Classify Anime Character Faces in ImagesCode0
Adversarial Feature Augmentation for Cross-domain Few-shot ClassificationCode1
Transfer Learning Application of Self-supervised Learning in ARPES0
Transductive Decoupled Variational Inference for Few-Shot ClassificationCode1
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails0
Learning Primitive-aware Discriminative Representations for Few-shot Learning0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
IPNET:Influential Prototypical Networks for Few Shot Learning0
Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning0
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification0
Open Long-Tailed Recognition in a Dynamic World0
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive LearningCode1
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural NetworksCode1
Limits of an AI program for solving college math problems0
Domain-invariant Prototypes for Semantic Segmentation0
Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray ImagesCode0
Advancing Plain Vision Transformer Towards Remote Sensing Foundation ModelCode2
Preserving Fine-Grain Feature Information in Classification via Entropic RegularizationCode0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning0
Atlas: Few-shot Learning with Retrieval Augmented Language ModelsCode2
Convolutional Ensembling based Few-Shot Defect Detection Technique0
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents0
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of MindCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient's PerspectiveCode0
Free-HeadGAN: Neural Talking Head Synthesis with Explicit Gaze Control0
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelCode2
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence0
Few-shot Adaptation Works with UnpredicTable DataCode1
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge0
Conservative Generator, Progressive Discriminator: Coordination of Adversaries in Few-shot Incremental Image Synthesis0
Boosting Point-BERT by Multi-choice TokensCode0
BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection0
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image ClassificationCode1
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification0
Kernel Relative-prototype Spectral Filtering for Few-shot LearningCode0
Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence0
Rethinking Few-Shot Object Detection on a Multi-Domain BenchmarkCode1
Language Model CascadesCode2
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
Similarity of Pre-trained and Fine-tuned Representations0
Self-Supervision Can Be a Good Few-Shot LearnerCode1
On the cross-lingual transferability of multilingual prototypical models across NLU tasks0
STT: Soft Template Tuning for Few-Shot Adaptation0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification0
Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression RecognitionCode1
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned RepresentationsCode0
Segment-level Metric Learning for Few-shot Bioacoustic Event DetectionCode1
Tree Structure-Aware Few-Shot Image Classification via Hierarchical AggregationCode1
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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