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 13511400 of 2964 papers

TitleStatusHype
Function Contrastive Learning of Transferable Representations0
Head2HeadFS: Video-based Head Reenactment with Few-shot Learning0
HetMAML: Task-Heterogeneous Model-Agnostic Meta-Learning for Few-Shot Learning Across Modalities0
HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis0
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data0
Demystifying Prompts in Language Models via Perplexity Estimation0
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners0
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes0
Hierarchical end-to-end autonomous navigation through few-shot waypoint detection0
Cut out the annotator, keep the cutout: better segmentation with weak supervision0
Hierarchical Knowledge Distillation on Text Graph for Data-limited Attribute Inference0
Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection0
Hierarchical Material Recognition from Local Appearance0
Hierarchical Meta Learning0
Customize Your Own Paired Data via Few-shot Way0
Beyond CLIP Generalization: Against Forward&Backward Forgetting Adapter for Continual Learning of Vision-Language Models0
Learning Multi-level Weight-centric Features for Few-shot Learning0
High-level semantic feature matters few-shot unsupervised domain adaptation0
Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN0
HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems0
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes0
IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning0
Interpolating Convolutional Neural Networks Using Batch Normalization0
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers0
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning0
FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing0
How Reliable AI Chatbots are for Disease Prediction from Patient Complaints?0
How Secure Are Large Language Models (LLMs) for Navigation in Urban Environments?0
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography0
How to fine-tune deep neural networks in few-shot learning?0
Curvature Generation in Curved Spaces for Few-Shot Learning0
Curvature: A signature for Action Recognition in Video Sequences0
Automatic Identification of Coal and Rock/Gangue Based on DenseNet and Gaussian Process0
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?0
A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarios0
From User Preferences to Optimization Constraints Using Large Language Models0
DGP-Net: Dense Graph Prototype Network for Few-Shot SAR Target Recognition0
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT30
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning0
From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs0
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning0
All You Need in Knowledge Distillation Is a Tailored Coordinate System0
A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (MedIMeta)0
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems0
Epistemic Graph: A Plug-And-Play Module For Hybrid Representation Learning0
Hyperbolic Dual Feature Augmentation for Open-Environment0
CryoMAE: Few-Shot Cryo-EM Particle Picking with Masked Autoencoders0
Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation0
From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget0
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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