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 9276–9300 of 10307 papers

TitleStatusHype
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
Efficient Neural Network Compression via Transfer Learning for Industrial Optical Inspection—0
Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New ExperimentsCode0
Domain-Invariant Projection Learning for Zero-Shot Recognition—0
A neural network to classify metaphorical violence on cable news—0
Transfer Learning versus Multi-agent Learning regarding Distributed Decision-Making in Highway Traffic—0
KTAN: Knowledge Transfer Adversarial Network—0
Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification—0
Deep Transfer Reinforcement Learning for Text SummarizationCode0
Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-—0
Stop Illegal Comments: A Multi-Task Deep Learning Approach—0
Named-Entity Linking Using Deep Learning For Legal Documents: A Transfer Learning Approach—0
Theoretical Guarantees of Transfer Learning—0
An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models—0
Cats or CAT scans: transfer learning from natural or medical image source datasets?Code0
Policy Transfer with Strategy OptimizationCode0
Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder—0
Bird Species Classification using Transfer Learning with Multistage TrainingCode0
A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem—0
Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel AttacksCode0
Multi-Source Cross-Lingual Model Transfer: Learning What to ShareCode0
Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces—0
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling—0
Survival prediction using ensemble tumor segmentation and transfer learningCode0
Dynamics and Reachability of Learning Tasks—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