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 10151–10175 of 10307 papers

TitleStatusHype
Minimally Supervised Feature Selection for Classification (Master's Thesis, University Politehnica of Bucharest)—0
Reuse of Neural Modules for General Video Game Playing—0
What can we learn about CNNs from a large scale controlled object dataset?—0
Unsupervised Domain Adaptation for Zero-Shot Learning—0
Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection—0
Confidence Preserving Machine for Facial Action Unit Detection—0
Bayesian Model Adaptation for Crowd Counts—0
The Multiverse Loss for Robust Transfer Learning—0
Homophily and missing links in citation networks—0
Integrating Deep Features for Material Recognition—0
Actor-Mimic: Deep Multitask and Transfer Reinforcement LearningCode0
Multilingual Relation Extraction using Compositional Universal SchemaCode0
Transfer Learning for Speech and Language Processing—0
Net2Net: Accelerating Learning via Knowledge TransferCode0
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization—0
AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation—0
Natural Language Object RetrievalCode0
Representational Distance Learning for Deep Neural Networks—0
Basic Level Categorization Facilitates Visual Object Recognition—0
Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism—0
Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random ForestsCode0
Prediction-Adaptation-Correction Recurrent Neural Networks for Low-Resource Language Speech Recognition—0
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks—0
Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation—0
Transfer Learning from Deep Features for Remote Sensing and Poverty MappingCode0
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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