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 17511800 of 2964 papers

TitleStatusHype
Towards Practical Few-shot Federated NLP0
Explicit Knowledge Transfer for Weakly-Supervised Code Generation0
PatchMix Augmentation to Identify Causal Features in Few-shot Learning0
Few-shot Query-Focused Summarization with Prefix-Merging0
Disentangled Generation with Information Bottleneck for Few-Shot Learning0
Revisiting Distance Metric Learning for Few-Shot Natural Language Classification0
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image ClassificationCode0
A Maximum Log-Likelihood Method for Imbalanced Few-Shot Learning TasksCode0
Adaptive Prototypical NetworksCode0
Karyotype AI for Precision OncologyCode0
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning0
Self-Transriber: Few-shot Lyrics Transcription with Self-training0
ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer EvaluationCode0
Technical Report on Neural Language Models and Few-Shot Learning for Systematic Requirements Processing in MDSE0
Few-shot Learning for Multi-modal Social Media Event FilteringCode0
Interpretable Few-shot Learning with Online Attribute Selection0
On Measuring the Intrinsic Few-Shot Hardness of DatasetsCode0
MEAL: Stable and Active Learning for Few-Shot PromptingCode0
Few-Shot Learning for Biometric Verification0
Few-shot Classification with Hypersphere Modeling of Prototypes0
ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning0
Robust Few-shot Learning Without Using any Adversarial SamplesCode0
tSF: Transformer-based Semantic Filter for Few-Shot LearningCode0
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective0
On the Informativeness of Supervision Signals0
Two-stage LLM Fine-tuning with Less Specialization and More Generalization0
SAGE: Saliency-Guided Mixup with Optimal Rearrangements0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
GPS: Genetic Prompt Search for Efficient Few-shot LearningCode0
Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition0
Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative EmbeddingsCode0
STPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification0
Efficient few-shot learning for pixel-precise handwritten document layout analysis0
Dictionary-Assisted Supervised Contrastive LearningCode0
Fusion-based Few-Shot Morphing Attack Detection and FingerprintingCode0
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationshipsCode0
UTILIZING FEDERATED LEARNING AND META LEARNING FOR FEW-SHOT LEARNING ON EDGE DEVICES0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
A Task-aware Dual Similarity Network for Fine-grained Few-shot LearningCode0
Graph Few-shot Learning with Task-specific StructuresCode0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification0
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models0
Self-Supervised Representation Learning for CAD0
LAVA: Label-efficient Visual Learning and AdaptationCode0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation0
Aligning MAGMA by Few-Shot Learning and Finetuning0
Conditional Neural Processes for Molecules0
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold0
Flame-state monitoring based on very low number of visible or infrared images via few-shot learning0
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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