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 1601–1625 of 10307 papers

TitleStatusHype
Accurate Prostate Cancer Detection and Segmentation on Biparametric MRI using Non-local Mask R-CNN with Histopathological Ground Truth—0
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos—0
A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs—0
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach—0
Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset—0
Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends—0
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Artificial Intelligence for Dementia Research Methods Optimization—0
Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review—0
A Free-Energy Principle for Representation Learning—0
CommonCanvas: Open Diffusion Models Trained on Creative-Commons Images—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
A Framework of Meta Functional Learning for Regularising Knowledge Transfer—0
Artificial Bee Colony optimization of Deep Convolutional Neural Networks in the context of Biomedical Imaging—0
Actor Critic with Differentially Private Critic—0
A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy—0
A Robust Illumination-Invariant Camera System for Agricultural Applications—0
A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach—0
A Robust Ensemble Model for Patasitic Egg Detection and Classification—0
A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic—0
A Framework for Hierarchical Multilingual Machine Translation—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