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 901950 of 2964 papers

TitleStatusHype
Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion0
Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations0
Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning0
CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models0
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?0
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy0
Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting0
External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery0
Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning0
Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Media0
Few-Shot Learning with Adaptive Weight Masking in Conditional GANs0
Facial Landmark Correlation Analysis0
FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learning0
Clinical Risk Prediction Using Language Models: Benefits And Considerations0
Few-shot Learning with Meta Metric Learners0
Few Shot Learning with Simplex0
Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentation0
Enhancing Multi-hop Reasoning in Vision-Language Models via Self-Distillation with Multi-Prompt Ensembling0
Clip4Retrofit: Enabling Real-Time Image Labeling on Edge Devices via Cross-Architecture CLIP Distillation0
Can GPT tell us why these images are synthesized? Empowering Multimodal Large Language Models for Forensics0
Generalized Adaptation for Few-Shot Learning0
Enhancing Instance-Level Image Classification with Set-Level Labels0
Fast Task Adaptation for Few-Shot Learning0
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot0
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging0
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners0
Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules0
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches0
Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation0
Can Explanations Be Useful for Calibrating Black Box Models?0
Few-shot Learning using Data Augmentation and Time-Frequency Transformation for Time Series Classification0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping0
Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability0
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions0
Annotation-Efficient Untrimmed Video Action Recognition0
Enhanced Urdu Intent Detection with Large Language Models and Prototype-Informed Predictive Pipelines0
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance0
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios0
English-Malay Word Embeddings Alignment for Cross-lingual Emotion Classification with Hierarchical Attention Network0
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models0
CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM0
Enabling the Network to Surf the Internet0
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks0
Enabling ISP-less Low-Power Computer Vision0
Few-Shot Learning Through an Information Retrieval Lens0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification0
Few-shot learning using pre-training and shots, enriched by pre-trained samples0
Show:102550
← PrevPage 19 of 60Next →

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