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 7401–7425 of 10307 papers

TitleStatusHype
A Transfer Learning Approach for Dialogue Act Classification of GitHub Issue CommentsCode0
Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications—0
Detecting Social Media Manipulation in Low-Resource Languages—0
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning—0
Low-Resource Adaptation of Neural NLP Models—0
Predictive Analysis of Diabetic Retinopathy with Transfer Learning—0
Robustness and Diversity Seeking Data-Free Knowledge DistillationCode0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
Deep Transfer Learning for Automated Diagnosis of Skin Lesions from PhotographsCode0
Exploring Multimodal Features and Fusion Strategies for Analyzing Disaster Tweets—0
Domain Adaptive Person Re-Identification via Coupling OptimizationCode0
An analysis of the transfer learning of convolutional neural networks for artistic images—0
Language Model is All You Need: Natural Language Understanding as Question Answering—0
Intra-Domain Task-Adaptive Transfer Learning to Determine Acute Ischemic Stroke Onset Time—0
DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech DetectionCode0
Autoencoding Features for Aviation Machine Learning Problems—0
Meta-learning Transferable Representations with a Single Target Domain—0
Developing High Quality Training Samples for Deep Learning Based Local Climate Zone Classification in Korea—0
Unsupervised Attention Based Instance Discriminative Learning for Person Re-IdentificationCode0
"You eat with your eyes first": Optimizing Yelp Image Advertising—0
Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning—0
Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering—0
A Deep Learning Study on Osteosarcoma Detection from Histological Images—0
Distilling Structured Knowledge for Text-Based Relational Reasoning—0
Quality In, Quality Out: Learning from Actual Mistakes—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