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 2526–2550 of 10307 papers

TitleStatusHype
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community—0
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising—0
A deep convolutional neural network for classification of Aedes albopictus mosquitoes—0
Benchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset—0
Benchmarking Image Embeddings for E-Commerce: Evaluating Off-the Shelf Foundation Models, Fine-Tuning Strategies and Practical Trade-offs—0
Transfer Learning Using Classification Layer Features of CNN—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches—0
Defining Boundaries: The Impact of Domain Specification on Cross-Language and Cross-Domain Transfer in Machine Translation—0
Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation—0
An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification—0
A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images—0
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images—0
A decision framework for selecting information-transfer strategies in population-based SHM—0
Hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and "transfer learning" technique—0
Benchmarking Algorithms for Automatic License Plate Recognition—0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application—0
Belief Tree Search for Active Object Recognition—0
Being Generous with Sub-Words towards Small NMT Children—0
An Efficient Evolutionary Deep Learning Framework Based on Multi-source Transfer Learning to Evolve Deep Convolutional Neural Networks—0
DEEVA: A Deep Learning and IoT Based Computer Vision System to Address Safety and Security of Production Sites in Energy Industry—0
Defining Image Memorability using the Visual Memory Schema—0
Behavior Priors for Efficient Reinforcement Learning—0
An Optimized Ensemble Deep Learning Model For Brain Tumor Classification—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