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 8901–8950 of 10307 papers

TitleStatusHype
Representation Similarity Analysis for Efficient Task taxonomy & Transfer LearningCode1
Attention-based Transfer Learning for Brain-computer Interface—0
Scene Graph Prediction with Limited LabelsCode0
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning—0
Lung Nodule Classification using Deep Local-Global NetworksCode0
SocialIQA: Commonsense Reasoning about Social InteractionsCode0
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
Active Adversarial Domain Adaptation—0
Double Transfer Learning for Breast Cancer Histopathologic Image Classification—0
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning—0
HAKE: Human Activity Knowledge EngineCode1
Digging Deeper into Egocentric Gaze Prediction—0
Scalable Cross-Lingual Transfer of Neural Sentence Embeddings—0
Cramnet: Layer-wise Deep Neural Network Compression with Knowledge Transfer from a Teacher Network—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
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification—0
Knowledge Squeezed Adversarial Network Compression—0
Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep LearningCode0
A New GAN-based End-to-End TTS Training Algorithm—0
Graphonomy: Universal Human Parsing via Graph Transfer LearningCode1
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer LearningCode0
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
Heterogeneous Multi-task Metric Learning across Multiple Domains—0
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning—0
From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal ImagesCode0
Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain—0
On The Power of Curriculum Learning in Training Deep NetworksCode0
The Information Complexity of Learning Tasks, their Structure and their Distance—0
Transfer Learning for Performance Modeling of Deep Neural Network SystemsCode0
Transfer Learning with Sparse Associative Memories—0
A Many Objective Optimization Approach for Transfer Learning in EEG Classification—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Resource Efficient 3D Convolutional Neural NetworksCode0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
Learning Implicit Generative Models by Matching Perceptual Features—0
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian OptimizationCode0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—0
Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval—0
Cross-lingual transfer learning for spoken language understanding—0
Correlation Congruence for Knowledge DistillationCode0
Identifying disease-free chest X-ray images with deep transfer learning—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