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 13011350 of 2964 papers

TitleStatusHype
Cap2Aug: Caption guided Image to Image data Augmentation0
An AI-Powered VVPAT Counter for Elections in IndiaCode0
Demystifying Prompts in Language Models via Perplexity Estimation0
EPCL: Frozen CLIP Transformer is An Efficient Point Cloud EncoderCode1
JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning0
Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural NetworksCode0
Few-shot Medical Image Segmentation with Cycle-resemblance Attention0
Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology0
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation0
Can In-context Learners Learn a Reasoning Concept from Demonstrations?Code0
Few-Shot Nested Named Entity Recognition0
Finetune like you pretrain: Improved finetuning of zero-shot vision modelsCode1
Towards Practical Few-shot Federated NLP0
Explicit Knowledge Transfer for Weakly-Supervised Code Generation0
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image ClassificationCode1
Disentangled Generation with Information Bottleneck for Few-Shot Learning0
Better Generalized Few-Shot Learning Even Without Base DataCode1
PatchMix Augmentation to Identify Causal Features in Few-shot Learning0
Few-shot Query-Focused Summarization with Prefix-Merging0
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image ClassificationCode0
RankDNN: Learning to Rank for Few-shot LearningCode1
Revisiting Distance Metric Learning for Few-Shot Natural Language Classification0
A Maximum Log-Likelihood Method for Imbalanced Few-Shot Learning TasksCode0
Adaptive Prototypical NetworksCode0
TEMPERA: Test-Time Prompting via Reinforcement LearningCode1
Karyotype AI for Precision OncologyCode0
Self-Transriber: Few-shot Lyrics Transcription with Self-training0
DASECount: Domain-Agnostic Sample-Efficient Wireless Indoor Crowd Counting via Few-shot Learning0
ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer EvaluationCode0
Few-shot Learning for Multi-modal Social Media Event FilteringCode0
Technical Report on Neural Language Models and Few-Shot Learning for Systematic Requirements Processing in MDSE0
Interpretable Few-shot Learning with Online Attribute Selection0
On Measuring the Intrinsic Few-Shot Hardness of DatasetsCode0
MEAL: Stable and Active Learning for Few-Shot PromptingCode0
QAmeleon: Multilingual QA with Only 5 ExamplesCode1
Retrieval-Augmented Generative Question Answering for Event Argument ExtractionCode1
AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERTCode1
Enhancing Few-shot Image Classification with Cosine TransformerCode1
Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud LearningCode1
Few-Shot Learning for Biometric Verification0
Few-shot Classification with Hypersphere Modeling of Prototypes0
miCSE: Mutual Information Contrastive Learning for Low-shot Sentence EmbeddingsCode1
ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning0
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot LearningCode1
Large Language Models Are Human-Level Prompt EngineersCode3
Robust Few-shot Learning Without Using any Adversarial SamplesCode0
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective0
tSF: Transformer-based Semantic Filter for Few-Shot LearningCode0
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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