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 7101–7125 of 10307 papers

TitleStatusHype
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review—0
Corona-Nidaan: lightweight deep convolutional neural network for chest X-Ray based COVID-19 infection detectionCode0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models—0
Short Text Clustering with Transformers—0
Ultrasound Image Classification using ACGAN with Small Training DatasetCode0
Cross-domain Activity Recognition via Substructural Optimal Transport—0
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection—0
Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning—0
Gesture Recognition in Robotic Surgery: a Review—0
A Petri Dish for Histopathology Image Analysis—0
Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images—0
Domain Adaptation by Topology Regularization—0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
Self-Calibrating Indoor Localization with Crowdsourcing Fingerprints and Transfer Learning—0
Adversarial Vulnerability of Active Transfer Learning—0
Performance Evaluation of Convolutional Neural Networks for Gait Recognition—0
3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware—0
Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets—0
Network-Agnostic Knowledge Transfer for Medical Image Segmentation—0
MinConvNets: A new class of multiplication-less Neural Networks—0
HANA: A HAndwritten NAme Database for Offline Handwritten Text RecognitionCode0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets—0
HASOCOne@FIRE-HASOC2020: Using BERT and Multilingual BERT models for Hate Speech DetectionCode0
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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