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 19011950 of 2964 papers

TitleStatusHype
Bilingual Sexism Classification: Fine-Tuned XLM-RoBERTa and GPT-3.5 Few-Shot Learning0
Bioacoustic Event Detection with prototypical networks and data augmentation0
BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine0
FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair0
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting0
Boosting Few-Shot Learning via Attentive Feature Regularization0
Boosting Few-Shot Learning With Adaptive Margin Loss0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification0
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning0
Fewer is More: Boosting LLM Reasoning with Reinforced Context Pruning0
Boosting Meta-Training with Base Class Information for Few-Shot Learning0
Boosting Supervision with Self-Supervision for Few-shot Learning0
Boosting Transductive Few-Shot Fine-Tuning With Margin-Based Uncertainty Weighting and Probability Regularization0
Language Models are Few-shot Learners for Prognostic Prediction0
Brain-inspired global-local learning incorporated with neuromorphic computing0
Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning0
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning0
Budget-aware Few-shot Learning via Graph Convolutional Network0
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models0
Cabrita: closing the gap for foreign languages0
CAFENet: Class-Agnostic Few-Shot Edge Detection Network0
C^*-algebra Net: A New Approach Generalizing Neural Network Parameters to C^*-algebra0
Calibrated neighborhood aware confidence measure for deep metric learning0
CalliffusionV2: Personalized Natural Calligraphy Generation with Flexible Multi-modal Control0
CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning0
CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM0
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models0
Can Explanations Be Useful for Calibrating Black Box Models?0
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging0
Can GPT tell us why these images are synthesized? Empowering Multimodal Large Language Models for Forensics0
Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Media0
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?0
Catalysis distillation neural network for the few shot open catalyst challenge0
Causal Interventions-based Few-Shot Named Entity Recognition0
CellularLint: A Systematic Approach to Identify Inconsistent Behavior in Cellular Network Specifications0
Centroid-based deep metric learning for speaker recognition0
Chain-of-Thought Textual Reasoning for Few-shot Temporal Action Localization0
Channel Phase Processing in Wireless Networks for Human Activity Recognition0
Channel-Spatial-Based Few-Shot Bird Sound Event Detection0
ChatGPT and general-purpose AI count fruits in pictures surprisingly well0
ChatGPT for Arabic Grammatical Error Correction0
Check-worthy Claim Detection across Topics for Automated Fact-checking0
CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems0
Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks0
Class Imbalance in Few-Shot Learning0
Class-Incremental Few-Shot Event Detection0
Class Interference Regularization0
Class-Specific Channel Attention for Few-Shot Learning0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion0
CLEAN-EVAL: Clean Evaluation on Contaminated Large Language Models0
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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