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 1626–1650 of 10307 papers

TitleStatusHype
Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection—0
Activity Recognition and Prediction in Real Homes—0
Argument Novelty and Validity Assessment via Multitask and Transfer Learning—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
A Framework for Fast Scalable BNN Inference using Googlenet and Transfer Learning—0
Accumulating Knowledge for Lifelong Online Learning—0
Comparison of different CNNs for breast tumor classification from ultrasound images—0
Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses—0
Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses—0
A foundational neural operator that continuously learns without forgetting—0
Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols—0
Are We Ready for Out-of-Distribution Detection in Digital Pathology?—0
A Foreground Inference Network for Video Surveillance Using Multi-View Receptive Field—0
ActivityCLIP: Enhancing Group Activity Recognition by Mining Complementary Information from Text to Supplement Image Modality—0
Comparison of fine-tuning strategies for transfer learning in medical image classification—0
A Food Photography App with Image Recognition for Thai Food—0
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications—0
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks—0
A Review on Discriminative Self-supervised Learning Methods in Computer Vision—0
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery—0
A Foliated View of Transfer Learning—0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
A review of sentiment analysis research in Arabic language—0
A Review of Deep Transfer Learning and Recent Advancements—0
A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection—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