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 3601–3625 of 10307 papers

TitleStatusHype
Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response—0
Ensemble Transfer Learning of Elastography and B-mode Breast Ultrasound Images—0
A multi-source approach for Breton–French hybrid machine translation—0
Classification of Industrial Control Systems screenshots using Transfer Learning—0
Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches—0
Entropy optimized semi-supervised decomposed vector-quantized variational autoencoder model based on transfer learning for multiclass text classification and generation—0
EnvBridge: Bridging Diverse Environments with Cross-Environment Knowledge Transfer for Embodied AI—0
Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfer—0
Classification of Melanocytic Nevus Images using BigTransfer (BiT)—0
Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory—0
Epigenomic language models powered by Cerebras—0
Epistemic Errors of Imperfect Multitask Learners When Distributions Shift—0
EPITA-ADAPT at SemEval-2019 Task 3: Detecting emotions in textual conversations using deep learning models combination—0
Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach—0
Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain—0
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption—0
Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation—0
Classification of Skin Disease Using Transfer Learning in Convolutional Neural Networks—0
Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition—0
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning—0
A Primer on Pretrained Multilingual Language Models—0
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition—0
A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems—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