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 7026–7050 of 10307 papers

TitleStatusHype
Adapting TTS models For New Speakers using Transfer Learning—0
Breast Cancer Image Classification Method Based on Deep Transfer Learning—0
SSDL: Self-Supervised Domain Learning for Improved Face Recognition—0
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS—0
Breast Lump Detection and Localization with a Tactile Glove Using Deep Learning—0
SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfer—0
SSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts—0
SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks—0
Bias and Generalizability of Foundation Models across Datasets in Breast Mammography—0
Breast mass detection in digital mammography based on anchor-free architecture—0
Stable Diffusion Dataset Generation for Downstream Classification Tasks—0
Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning—0
Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition—0
Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval—0
Stacked transfer learning for tropical cyclone intensity prediction—0
Stacking for Transfer Learning—0
Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection—0
Star-Graph Multimodal Matching Component Analysis for Data Fusion and Transfer Learning—0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
StARS DCM: A Sleep Stage-Decoding Forehead EEG Patch for Real-time Modulation of Sleep Physiology—0
State Classification with CNN—0
State-of-the-art and gaps for deep learning on limited training data in remote sensing—0
Station-to-User Transfer Learning: Towards Explainable User Clustering Through Latent Trip Signatures Using Tidal-Regularized Non-Negative Matrix Factorization—0
Statistical Deficiency for Task Inclusion Estimation—0
Statistical Hardware Design With Multi-model Active Learning—0
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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