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 5801–5850 of 10307 papers

TitleStatusHype
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
Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network—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
Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes—0
Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification—0
Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images—0
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation—0
Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy—0
Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem—0
Multitask Learning for Arabic Offensive Language and Hate-Speech Detection—0
Multi-task Learning for Identification of Porcelain in Song and Yuan Dynasties—0
Multi-Task Learning for Joint Semantic Role and Proto-Role Labeling—0
Multi-task Learning for Multilingual Neural Machine Translation—0
Multi-task learning for natural language processing in the 2020s: where are we going?—0
Multitask learning for recognizing stress and depression in social media—0
Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks—0
Multi-task learning from fixed-wing UAV images for 2D/3D city modeling—0
Multi-Task Learning of Query Intent and Named Entities using Transfer Learning—0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention—0
Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning—0
Multi-task Learning over Graph Structures—0
Multi-Task Learning with Incomplete Data for Healthcare—0
Multi-task multi-constraint differential evolution with elite-guided knowledge transfer for coal mine integrated energy system dispatching—0
Multi-task Optimization Based Co-training for Electricity Consumption Prediction—0
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning—0
Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model—0
Multi-Task Regularization with Covariance Dictionary for Linear Classifiers—0
Multi-Task Reinforcement Learning for Quadrotors—0
Multi-task Representation Learning with Stochastic Linear Bandits—0
Multi-task Self-Supervised Learning for Human Activity Detection—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Multi-Task Supervised Pretraining for Neural Domain Adaptation—0
Multi-task transfer learning for finding actionable information from crisis-related messages on social media—0
Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response—0
Multivariate and Online Transfer Learning with Uncertainty Quantification—0
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces—0
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision—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