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 3501–3525 of 10307 papers

TitleStatusHype
Deep Embedding Kernel—0
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
End-to-end Spoken Conversational Question Answering: Task, Dataset and Model—0
End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning—0
End-to-end transfer learning for speaker-independent cross-language and cross-corpus speech emotion recognition—0
Adaptive Variants of Optimal Feedback Policies—0
End-to-end Whispered Speech Recognition with Frequency-weighted Approaches and Pseudo Whisper Pre-training—0
Energy Clustering for Unsupervised Person Re-identification—0
Energy Consumption Reduction for UAV Trajectory Training : A Transfer Learning Approach—0
Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models—0
Energy efficient distributed analytics at the edge of the network for IoT environments—0
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities—0
Energy Predictive Models with Limited Data using Transfer Learning—0
Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods—0
Engagement Measurement Based on Facial Landmarks and Spatial-Temporal Graph Convolutional Networks—0
English-Basque Statistical and Neural Machine Translation—0
Enhanced Behavioral Cloning Based self-driving Car Using Transfer Learning—0
Deep Discriminative Fine-Tuning for Cancer Type Classification—0
Automatic detection of passable roads after floods in remote sensed and social media data—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan—0
Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning—0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—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