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 7951–7975 of 10307 papers

TitleStatusHype
Speech Synthesis for Low Resource Languages using Transliteration Enabled Transfer Learning—0
Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition—0
Multi-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning—0
Speech Tasks Relevant to Sleepiness Determined with Deep Transfer Learning—0
Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis—0
Multi-source Transfer Learning with Ensemble for Financial Time Series Forecasting—0
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models—0
Multi-Stage Deep Transfer Learning for EmIoT-enabled Human-Computer Interaction—0
Multi-Stage Framework with Refinement based Point Set Registration for Unsupervised Bi-Lingual Word Alignment—0
Multi-Stage Framework with Refinement Based Point Set Registration for Unsupervised Bi-Lingual Word Alignment—0
Multi-Stage Influence Function—0
Multi-Stage Pre-training for Low-Resource Domain Adaptation—0
Multi-stage RGB-based Transfer Learning Pipeline for Hand Activity Recognition—0
Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19—0
Multi-Stage Transfer Learning with an Application to Selection Process—0
Multi-step Estimation for Gradient-based Meta-learning—0
Multi-step learning and underlying structure in statistical models—0
AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale—0
Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network—0
AI Reasoning Systems: PAC and Applied Methods—0
AI-Powered Semantic Segmentation and Fluid Volume Calculation of Lung CT images in Covid-19 Patients—0
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling—0
Multi-Task and Transfer Learning for Federated Learning Applications—0
Multitask and Transfer Learning for Autotuning Exascale Applications—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