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 9576–9600 of 10307 papers

TitleStatusHype
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems—0
Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers—0
Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals—0
Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition—0
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction—0
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning—0
Recurrent Neural Network Encoder with Attention for Community Question Answering—0
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection—0
Cross-Cultural Transfer Learning for Text Classification—0
Cross-database non-frontal facial expression recognition based on transductive deep transfer learning—0
Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection—0
Recurrent Neural Network for MoonBoard Climbing Route Classification and Generation—0
Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird's-Eye View—0
Recurrent neural networks and transfer learning for elasto-plasticity in woven composites—0
Recurrent Neural Network Training with Dark Knowledge Transfer—0
Cross-domain Activity Recognition via Substructural Optimal Transport—0
Cross Domain Adaptation by Learning Partially Shared Classifiers and Weighting Source Data Points in the Shared Subspaces—0
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation—0
Cross-domain aspect extraction for sentiment analysis: a transductive learning approach—0
Cross-domain attribute representation based on convolutional neural network—0
Cross-domain Augmentation Networks for Click-Through Rate Prediction—0
Recursive Distillation for Open-Set Distributed Robot Localization—0
Cross-domain Deep Feature Combination for Bird Species Classification with Audio-visual Data—0
Cross Domain Emotion Recognition using Few Shot Knowledge Transfer—0
Cross-domain Feature Selection for Language Identification—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