SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2501–2525 of 10307 papers

TitleStatusHype
Augmenting Biomedical Named Entity Recognition with General-domain ResourcesCode0
Data-driven Prior Learning for Bayesian OptimisationCode0
First-frame Supervised Video Polyp Segmentation via Propagative and Semantic Dual-teacher NetworkCode0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
Finger Pose Estimation for Under-screen Fingerprint SensorCode0
FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image AnalysisCode0
AugFL: Augmenting Federated Learning with Pretrained ModelsCode0
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural FeaturesCode0
Fine-grained Sentiment Classification using BERTCode0
CUDA-GHR: Controllable Unsupervised Domain Adaptation for Gaze and Head RedirectionCode0
Fine-Grained Emotion Prediction by Modeling Emotion DefinitionsCode0
CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion RecognitionCode0
AMPL: A Data-Driven Modeling Pipeline for Drug DiscoveryCode0
A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service InferenceCode0
Fine-Grained Classification for Poisonous Fungi Identification with Transfer LearningCode0
CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report SummarizationCode0
Automated Behavioral Analysis Using Instance SegmentationCode0
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant SetupCode0
Few-Shot Learning for Image Classification of Common FloraCode0
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic GapCode0
Few-Shot Image Recognition With Knowledge TransferCode0
Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain StudyCode0
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learningCode0
Few-shot classification in Named Entity Recognition TaskCode0
Cross-View Policy Learning for Street NavigationCode0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified