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 5726–5750 of 10307 papers

TitleStatusHype
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
Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies—0
Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels—0
StEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic—0
Steerable Equivariant Representation Learning—0
STemGAN: spatio-temporal generative adversarial network for video anomaly detection—0
Step Out and Seek Around: On Warm-Start Training with Incremental Data—0
Stiff Transfer Learning for Physics-Informed Neural Networks—0
Stitching for Neuroevolution: Recombining Deep Neural Networks without Breaking Them—0
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning—0
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers—0
Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks—0
Stochastic Vision Transformers with Wasserstein Distance-Aware Attention—0
Stock and market index prediction using Informer network—0
Stop Illegal Comments: A Multi-Task Deep Learning Approach—0
STRATA: Word Boundaries & Phoneme Recognition From Continuous Urdu Speech using Transfer Learning, Attention, & Data Augmentation—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