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 7376–7400 of 10307 papers

TitleStatusHype
Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition—0
TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques—0
Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network—0
Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sources—0
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle—0
Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift—0
Transfer from Multiple Linear Predictive State Representations (PSR)—0
Transfer Generative Adversarial Networks (T-GAN)-based Terahertz Channel Modeling—0
Transfer Hashing with Privileged Information—0
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach—0
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets—0
Transfer in Deep Reinforcement Learning using Knowledge Graphs—0
Transfer in Reinforcement Learning via Regret Bounds for Learning Agents—0
Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection—0
Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data—0
Transfer Learning Across Fixed-Income Product Classes—0
Efficiently Reusing Old Models Across Languages via Transfer Learning—0
Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation—0
Transfer Learning across Networks for Collective Classification—0
Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes—0
Transfer Learning across Several Centuries: Machine and Historian Integrated Method to Decipher Royal Secretary's Diary—0
Transfer Learning Across Simulated Robots With Different Sensors—0
Transfer Learning Adapts to Changing PSD in Gravitational Wave Data—0
Transfer Learning Analysis of Variational Quantum Circuits—0
Transfer Learning and Augmentation for Word Sense Disambiguation—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