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

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Showing 9751–9775 of 10307 papers

TitleStatusHype
LOD1 3D City Model from LiDAR: The Impact of Segmentation Accuracy on Quality of Urban 3D Modeling and Morphology ExtractionCode0
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulenceCode0
LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and VectorsCode0
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 ChallengeCode0
Low-Cost Transfer Learning of Face TasksCode0
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain ResponsesCode0
Low Latency Privacy Preserving InferenceCode0
Low Resource Chat Translation: A Benchmark for Hindi–English Language PairCode0
LSTD: A Low-Shot Transfer Detector for Object DetectionCode0
Lung cancer detection from thoracic CT scans using an ensemble of deep learning modelsCode0
Lung Nodule Classification using Deep Local-Global NetworksCode0
Machine learning method for single trajectory characterizationCode0
Machine Learning Methods for Track Classification in the AT-TPCCode0
A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the WildCode0
Machine UnlearningCode0
Macsen: A Voice Assistant for Speakers of a Lesser Resourced LanguageCode0
Managing Household Waste through Transfer LearningCode0
Manifold Characteristics That Predict Downstream Task PerformanceCode0
Manifold Criterion Guided Transfer Learning via Intermediate Domain GenerationCode0
Manifold Embedded Knowledge Transfer for Brain-Computer InterfacesCode0
Manipulating Transfer Learning for Property InferenceCode0
Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer LearningCode0
Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and RecognitionCode0
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity RecognitionCode0
Masked Autoencoders are Efficient Continual Federated LearnersCode0
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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