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 25012550 of 2964 papers

TitleStatusHype
I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution0
ICDAR 2024 Competition on Few-Shot and Many-Shot Layout Segmentation of Ancient Manuscripts (SAM)0
Identification of emotions on Twitter during the 2022 electoral process in Colombia0
Identifying Fairness Issues in Automatically Generated Testing Content0
Identity Document to Selfie Face Matching Across Adolescence0
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System0
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications0
Impact of Aliasing on Generalization in Deep Convolutional Networks0
Impossible Triangle: What's Next for Pre-trained Language Models?0
Improved Few-Shot Visual Classification0
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases0
Improve Novel Class Generalization By Adaptive Feature Distribution for Few-Shot Learning0
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations0
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning0
Improving Few-Shot Learning using Composite Rotation based Auxiliary Task0
Improving Few-shot Learning with Weakly-supervised Object Localization0
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration0
Enhancing Few-Shot Image Classification with Unlabelled Examples0
Improving Few-Shot Visual Classification with Unlabelled Examples0
Improving In-Context Few-Shot Learning via Self-Supervised Training0
Improving Instruct Models for Free: A Study on Partial Adaptation0
Improving out-of-distribution generalization via multi-task self-supervised pretraining0
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction0
Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target0
In-BoXBART: Get Instructions into Biomedical Multi-task Learning0
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning0
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models0
In-Context Learning for Few-Shot Molecular Property Prediction0
Incremental Few-Shot Learning via Implanting and Compressing0
Incremental few-shot learning via vector quantization in deep embedded space0
Incremental Few-Shot Meta-Learning via Indirect Discriminant Alignment0
Incremental Few-Shot Object Detection0
Inductive Linear Probing for Few-shot Node Classification0
Infinite Mixture Prototypes for Few-Shot Learning0
Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition0
Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study0
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups0
Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning0
Injecting Text and Cross-lingual Supervision in Few-shot Learning from Self-Supervised Models0
In-Memory Nearest Neighbor Search with FeFET Multi-Bit Content-Addressable Memories0
Instance-based Max-margin for Practical Few-shot Recognition0
Instance-Conditioned GAN Data Augmentation for Representation Learning0
IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning0
Integrating Domain Knowledge into Large Language Models for Enhanced Fashion Recommendations0
Integrating Large Language Models with Internet of Things Applications0
Intelligent Known and Novel Aircraft Recognition -- A Shift from Classification to Similarity Learning for Combat Identification0
Interpolating Convolutional Neural Networks Using Batch Normalization0
Interpretable Concept-based Prototypical Networks for Few-Shot Learning0
Interpretable Few-shot Learning with Online Attribute Selection0
Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction0
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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