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 9701–9750 of 10307 papers

TitleStatusHype
Grouping-By-ID: Guarding Against Adversarial Domain Shifts—0
Transfer Learning on Manifolds via Learned Transport Operators—0
NerveNet: Learning Structured Policy with Graph Neural NetworksCode0
Sequence Transfer Learning for Neural Decoding—0
Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic Resonance Images—0
Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data—0
Scalable Multi-Domain Dialogue State TrackingCode0
Learning More Universal Representations for Transfer-LearningCode0
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationCode0
Stratified Transfer Learning for Cross-domain Activity Recognition—0
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer—0
Transfer Regression via Pairwise Similarity Regularization—0
Enhance Visual Recognition under Adverse Conditions via Deep Networks—0
Transfer Learning for OCRopus Model Training on Early Printed BooksCode0
Towards Deep Modeling of Music Semantics using EEG Regularizers—0
Feature-Based Transfer Learning for Network SecurityCode0
Learning Modality-Invariant Representations for Speech and Images—0
Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications—0
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery—0
Visual aesthetic analysis using deep neural network: model and techniques to increase accuracy without transfer learning—0
Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing—0
Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition—0
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property PredictionCode0
Distribution-Based Categorization of Classifier Transfer Learning—0
Pose-Normalized Image Generation for Person Re-identificationCode2
Automated Pruning for Deep Neural Network Compression—0
Leaf Identification Using a Deep Convolutional Neural Network—0
Learning Independent Causal MechanismsCode0
Automatic Recognition of Coal and Gangue based on Convolution Neural Network—0
FBK’s Multilingual Neural Machine Translation System for IWSLT 2017—0
Linguistic approach based Transfer Learning for Sentiment Classification in Hindi—0
Learning to Model the Tail—0
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision ProcessesCode0
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks—0
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis—0
Graph Distillation for Action Detection with Privileged ModalitiesCode0
Label Efficient Learning of Transferable Representations across Domains and Tasks—0
Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness—0
Transferring Autonomous Driving Knowledge on Simulated and Real Intersections—0
Towards Alzheimer's Disease Classification through Transfer LearningCode0
Transfer Learning with Binary Neural Networks—0
Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay—0
Modeling Information Flow Through Deep Neural Networks—0
Deep Learning for identifying radiogenomic associations in breast cancer—0
Learning to Rank based on Analogical Reasoning—0
Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network—0
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ—0
Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks—0
Exposing Computer Generated Images by Using Deep Convolutional Neural Networks—0
Learning to cluster in order to transfer across domains and tasksCode0
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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