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 8576–8600 of 10307 papers

TitleStatusHype
A Universal Parent Model for Low-Resource Neural Machine Translation Transfer—0
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
musicnn: Pre-trained convolutional neural networks for music audio taggingCode0
Not again! Data Leakage in Digital Pathology—0
A Knowledge Transfer Framework for Differentially Private Sparse Learning—0
Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios—0
A Gated Self-attention Memory Network for Answer SelectionCode0
Semantic and Visual Similarities for Efficient Knowledge Transfer in CNN Training—0
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling—0
Differentially Private Meta-Learning—0
Semi-supervised Vector-valued Learning: Improved Bounds and AlgorithmsCode0
Estimating encoding models of cortical auditory processing using naturalistic stimuli and transfer learningCode0
Functional Annotation of Human Cognitive States using Graph Convolution Networks—0
Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices—0
Frustratingly Easy Natural Question Answering—0
From English to Code-Switching: Transfer Learning with Strong Morphological CluesCode0
Knowledge Transfer Graph for Deep Collaborative LearningCode0
Swapped Face Detection using Deep Learning and Subjective Assessment—0
The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale—0
Transfer of Temporal Logic Formulas in Reinforcement Learning—0
What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis—0
Transfer Reward Learning for Policy Gradient-Based Text Generation—0
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer—0
Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?—0
Transfer Learning Robustness in Multi-Class Categorization by Fine-Tuning Pre-Trained Contextualized Language ModelsCode0
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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