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

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Showing 2451–2500 of 2964 papers

TitleStatusHype
ReMP: Rectified Metric Propagation for Few-Shot Learning—0
Effective Few-Shot Classification with Transfer Learning—0
COVID-19 Surveillance through Twitter using Self-Supervised and Few Shot Learning—0
MATE: Plugging in Model Awareness to Task Embedding for Meta LearningCode0
Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive SummarizationCode0
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification—0
How to fine-tune deep neural networks in few-shot learning?—0
A Closer Look at the Training Strategy for Modern Meta-LearningCode0
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification—0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
Multi-scale Adaptive Task Attention Network for Few-Shot Learning—0
Annotation-Efficient Untrimmed Video Action Recognition—0
Is Support Set Diversity Necessary for Meta-Learning?—0
Data-Efficient Classification of Radio GalaxiesCode0
RNNP: A Robust Few-Shot Learning Approach—0
An Effective Anti-Aliasing Approach for Residual Networks—0
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation—0
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning—0
Probing Predictions on OOD Images via Nearest CategoriesCode0
Multimodal Prototypical Networks for Few-shot Learning—0
In-Memory Nearest Neighbor Search with FeFET Multi-Bit Content-Addressable Memories—0
Zero-shot Relation Classification from Side InformationCode0
A Nested Bi-level Optimization Framework for Robust Few Shot Learning—0
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography—0
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance—0
A Broad Dataset is All You Need for One-Shot Object Detection—0
Self-Supervised Learning from Contrastive Mixtures for Personalized Speech EnhancementCode0
Regularization of Distinct Strategies for Unsupervised Question GenerationCode0
The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT—0
SigNet: A Novel Deep Learning Framework for Radio Signal Classification—0
Malicious Requests Detection with Improved Bidirectional Long Short-term Memory Neural Networks—0
Few-shot Image Recognition with Manifolds—0
Task-Adaptive Feature Transformer for Few-Shot Segmentation—0
Learning to Learn Variational Semantic MemoryCode0
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation—0
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization—0
On the Utility of Active Instance Selection for Few-Shot Learning—0
ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning AlgorithmsCode0
Theoretical bounds on estimation error for meta-learning—0
Function Contrastive Learning of Transferable Meta-Representations—0
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization—0
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models—0
Few-shot Learning for Multi-label Intent Detection—0
Addressing the Real-world Class Imbalance Problem in Dermatology—0
Uncertainty-Aware Few-Shot Image Classification—0
Few-shot Learning for Spatial Regression—0
Learning Clusterable Visual Features for Zero-Shot Recognition—0
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent DetectionCode0
Representation learning from videos in-the-wild: An object-centric approach—0
Shot in the Dark: Few-Shot Learning with No Base-Class Labels—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91—Unverified
2gpt-4-0125-previewAccuracy52.49—Unverified
3gpt-3.5-turboAccuracy41.48—Unverified
4gpt-3.5-turboAccuracy37.06—Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63—Unverified
6yikuan8/Clinical-LongformerAccuracy25.55—Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06—Unverified
8yikuan8/Clinical-LongformerAccuracy25.04—Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92—Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27—Unverified
2SaSPA + CAL4-shot Accuracy48.3—Unverified
3Real-Guidance + CAL4-shot Accuracy41.5—Unverified
4CAL4-shot Accuracy40.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2—Unverified
2CALHarmonic mean35.2—Unverified
3Variational Prompt TuningHarmonic mean34.69—Unverified
4Real-Guidance + CALHarmonic mean34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7—Unverified
2TIM-GDAccuracy87.4—Unverified
3UNEM-GaussianAccuracy66.4—Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75—Unverified
2HCTransformers5 way 1~2 shot74.74—Unverified
3HyperShotAccuracy53.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7—Unverified
2Real-Guidance + CAL4-shot Accuracy44.3—Unverified
3CAL4-shot Accuracy42.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74—Unverified
2DPGNAcc67.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9—Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57—Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79—Unverified