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 9151–9175 of 10307 papers

TitleStatusHype
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set—0
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders—0
PAC Learning Guarantees Under Covariate Shift—0
Deep UL2DL: Channel Knowledge Transfer from Uplink to DownlinkCode0
Action Quality Assessment Across Multiple ActionsCode0
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal TrainingCode0
Few-shot classification in Named Entity Recognition TaskCode0
Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation—0
Transfer learning to model inertial confinement fusion experiments—0
When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets—0
Dynamic Transfer Learning for Named Entity Recognition—0
Considering Race a Problem of Transfer Learning—0
Towards Ophthalmologist Level Accurate Deep Learning System for OCT Screening and Diagnosis—0
Transfer Learning using Representation Learning in Massive Open Online Courses—0
ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
Secure Federated Transfer Learning—0
PIRC Net : Using Proposal Indexing, Relationships and Context for Phrase Grounding—0
Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models—0
Disjoint Label Space Transfer Learning with Common Factorised Space—0
A Survey of Unsupervised Deep Domain AdaptationCode0
OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation—0
An Embarrassingly Simple Approach for Knowledge DistillationCode0
ADARES: Adaptive Resource Management for Virtual Machines—0
A novel database of Children's Spontaneous Facial Expressions (LIRIS-CSE)—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