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 20512100 of 2964 papers

TitleStatusHype
MAML is a Noisy Contrastive Learner in ClassificationCode1
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning0
What's in a Measurement? Using GPT-3 on SemEval 2021 Task 8 -- MeasEval0
High-dimensional separability for one- and few-shot learning0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts0
Multimodal Few-Shot Learning with Frozen Language Models0
Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks0
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language ModelsCode1
Mutual-Information Based Few-Shot ClassificationCode1
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification TasksCode0
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot ClassificationCode1
Trainable Class Prototypes for Few-Shot Learning0
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video ClassificationCode0
Task Attended Meta-Learning for Few-Shot Learning0
Learning Graphs for Knowledge Transfer With Limited Labels0
Labeled From Unlabeled: Exploiting Unlabeled Data for Few-Shot Deep HDR Deghosting0
Mutual CRF-GNN for Few-Shot Learning0
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Episode Adaptive Embedding Networks for Few-shot LearningCode0
SPeCiaL: Self-Supervised Pretraining for Continual Learning0
Transductive Few-Shot Learning: Clustering is All You Need?Code1
ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning0
Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction0
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Few-shot learning of new sound classes for target sound extraction0
NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification0
Generate, Annotate, and Learn: NLP with Synthetic TextCode0
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction0
Learning to Affiliate: Mutual Centralized Learning for Few-shot ClassificationCode1
Attentional Meta-learners for Few-shot Polythetic ClassificationCode0
Tensor feature hallucination for few-shot learningCode0
Few-Shot Action Localization without Knowing BoundariesCode0
DETReg: Unsupervised Pretraining with Region Priors for Object DetectionCode1
Scaling Vision Transformers0
Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization0
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation0
DAMSL: Domain Agnostic Meta Score-based LearningCode0
Reordering Examples Helps during Priming-based Few-Shot LearningCode0
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?Code0
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations0
Personalizing Pre-trained Models0
Few-Shot Partial-Label Learning0
Learning to Learn Semantic Factors in Heterogeneous Image Classification0
Scalable Few-Shot Learning of Robust Biomedical Name RepresentationsCode0
Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement (Meta-learning : Classifying Messages into Unseen Emotional Categories)0
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic SegmentationCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Multi-Label Few-Shot Learning for Aspect Category Detection0
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning0
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling0
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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