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 21512200 of 2964 papers

TitleStatusHype
Thespian: Multi-Character Text Role-Playing Game Agents0
The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT0
The unreasonable effectiveness of few-shot learning for machine translation0
TinyMetaFed: Efficient Federated Meta-Learning for TinyML0
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection0
Topology-Aware CLIP Few-Shot Learning0
Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks0
A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning0
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing0
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework0
Towards a graph-based foundation model for network traffic analysis0
Towards an Unsupervised Method for Model Selection in Few-Shot Learning0
Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology0
Towards a vision foundation model for comprehensive assessment of Cardiac MRI0
Towards Evaluating Large Language Models for Graph Query Generation0
Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View0
Towards Few-Shot Fact-Checking via Perplexity0
Towards Few-Shot Learning in the Open World: A Review and Beyond0
Towards Generalized and Incremental Few-Shot Object Detection0
CAM-loss: Towards Learning Spatially Discriminative Feature Representations0
Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach0
Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models0
Towards Understanding the Cause of Error in Few-Shot Learning0
Towards Understanding the Relationship between In-context Learning and Compositional Generalization0
Towards Unified Prompt Tuning for Few-shot Learning0
Towards Unified Prompt Tuning for Few-shot Text Classification0
TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models0
TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography0
Trainable Class Prototypes for Few-Shot Learning0
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization0
Training microrobots to swim by a large language model0
TRAM: Benchmarking Temporal Reasoning for Large Language Models0
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning0
A Transductive Maximum Margin Classifier for Few-Shot Learning0
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents0
Transfer Learning Application of Self-supervised Learning in ARPES0
Transfer Learning for Power Outage Detection Task with Limited Training Data0
Transfer Learning in a Transductive Setting0
Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning0
Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly Detection in Videos0
On Understanding Attention-Based In-Context Learning for Categorical Data0
Transformers for Supervised Online Continual Learning0
Transforming Multimodal Models into Action Models for Radiotherapy0
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning0
TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification0
TREC: APT Tactic / Technique Recognition via Few-Shot Provenance Subgraph Learning0
True Few-Shot Learning with Prompts -- A Real-World Perspective0
TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking0
TSAL: Few-shot Text Segmentation Based on Attribute Learning0
Turkish Universal Conceptual Cognitive Annotation0
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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