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 4476–4500 of 10307 papers

TitleStatusHype
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
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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