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 2101–2150 of 2964 papers

TitleStatusHype
DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Network—0
DocumentNet: Bridging the Data Gap in Document Pre-Training—0
Does Correction Remain A Problem For Large Language Models?—0
Does Few-Shot Learning Help LLM Performance in Code Synthesis?—0
Does Few-shot Learning Suffer from Backdoor Attacks?—0
Domain Adaptation for Learning Generator from Paired Few-Shot Data—0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Domain Agnostic Few-shot Learning for Speaker Verification—0
Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction—0
Domain-invariant Prototypes for Semantic Segmentation—0
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection—0
Do Prompt-Based Models Really Understand the Meaning of Their Prompts?—0
Dropping Networks for Transfer Learning—0
DrugLLM: Open Large Language Model for Few-shot Molecule Generation—0
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning—0
Dual-channel Prototype Network for few-shot Classification of Pathological Images—0
Dual Context-Guided Continuous Prompt Tuning for Few-Shot Learning—0
Dynamic Conditional Networks for Few-Shot Learning—0
Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications—0
Dynamic Few-Shot Learning for Knowledge Graph Question Answering—0
Dynamic Input Structure and Network Assembly for Few-Shot Learning—0
Dynamic Memory Induction Networks for Few-Shot Text Classification—0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics—0
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing—0
ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning—0
EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation—0
EEML: Ensemble Embedded Meta-learning—0
Effective Few-Shot Classification with Transfer Learning—0
Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection—0
Efficacy of Synthetic Data as a Benchmark—0
Efficient and Reliable Vector Similarity Search Using Asymmetric Encoding with NAND-Flash for Many-Class Few-Shot Learning—0
Efficient Automatic Meta Optimization Search for Few-Shot Learning—0
TransMed: Large Language Models Enhance Vision Transformer for Biomedical Image Classification—0
Efficient few-shot learning for pixel-precise handwritten document layout analysis—0
Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning—0
Efficient Meta Learning via Minibatch Proximal Update—0
Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering—0
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization—0
Embedding Adaptation is Still Needed for Few-Shot Learning—0
Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learning—0
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning—0
Empirical Evaluation of Topic Zero- and Few-Shot Learning for Stance Dissonance Detection—0
Empowering Large Language Models for Textual Data Augmentation—0
EMR Coding with Semi-Parametric Multi-Head Matching Networks—0
Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values—0
Enabling hand gesture customization on wrist-worn devices—0
Enabling ISP-less Low-Power Computer Vision—0
Enabling the Network to Surf the Internet—0
English-Malay Word Embeddings Alignment for Cross-lingual Emotion Classification with Hierarchical Attention Network—0
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance—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