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 8951–8975 of 10307 papers

TitleStatusHype
Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification—0
An Evaluation of Transfer Learning for Classifying Sales Engagement Emails at Large Scale—0
Convolutional Neural Network and Transfer Learning for High Impedance Fault Detection—0
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning—0
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition—0
An Empirical Evaluation of Text Representation Schemes on Multilingual Social Web to Filter the Textual Aggression—0
Double Transfer Learning for Breast Cancer Histopathologic Image Classification—0
Active Adversarial Domain Adaptation—0
End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning—0
Digging Deeper into Egocentric Gaze Prediction—0
Cramnet: Layer-wise Deep Neural Network Compression with Knowledge Transfer from a Teacher Network—0
Scalable Cross-Lingual Transfer of Neural Sentence Embeddings—0
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch—0
Variational Information Distillation for Knowledge Transfer—0
Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound—0
Knowledge Squeezed Adversarial Network Compression—0
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification—0
Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep LearningCode0
A New GAN-based End-to-End TTS Training Algorithm—0
Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer LearningCode0
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning—0
Heterogeneous Multi-task Metric Learning across Multiple Domains—0
From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal ImagesCode0
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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