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 7226–7250 of 10307 papers

TitleStatusHype
Multimodal Transfer Learning-based Approaches for Retinal Vascular Segmentation—0
Transfer Learning Based Automatic Model Creation Tool For Resource Constraint Devices—0
The effectiveness of unsupervised subword modeling with autoregressive and cross-lingual phone-aware networks—0
Multi-Modal Depth Estimation Using Convolutional Neural Networks—0
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps—0
Multilingual Evidence Retrieval and Fact Verification to Combat Global Disinformation: The Power of PolyglotismCode0
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning—0
Transfer Learning Through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Images—0
Wasserstein Contrastive Representation Distillation—0
Learning Prediction Intervals for Model Performance—0
Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning—0
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation—0
Exploring Transfer Learning For End-to-End Spoken Language Understanding—0
Hypothesis Disparity Regularized Mutual Information Maximization—0
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interactionCode0
DeepGamble: Towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection—0
Decoupled Self Attention for Accurate One Stage Object DetectionCode0
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees—0
Warm Starting CMA-ES for Hyperparameter OptimizationCode0
Filtering DDoS Attacks from Unlabeled Network Traffic Data Using Online Deep Learning—0
Multilingual Transfer Learning for QA Using Translation as Data Augmentation—0
Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets—0
A Practical Approach towards Causality Mining in Clinical Text using Active Transfer Learning—0
Deep Learning Approach Combining Lightweight CNN Architecture with Transfer Learning: An Automatic Approach for the Detection and Recognition of Bangladeshi Banknotes—0
Distant Domain Transfer Learning for Medical Imaging—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