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 6101–6125 of 10307 papers

TitleStatusHype
A Robust Ensemble Model for Patasitic Egg Detection and Classification—0
High-Resolution Detection of Earth Structural Heterogeneities from Seismic Amplitudes using Convolutional Neural Networks with Attention layers—0
A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic—0
Cross-domain Named Entity Recognition via Graph Matching—0
Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines—0
Argument Novelty and Validity Assessment via Multitask and Transfer Learning—0
Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes—0
Hi Sigma, do I have the Coronavirus?: Call for a New Artificial Intelligence Approach to Support Health Care Professionals Dealing With The COVID-19 Pandemic—0
Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses—0
Self-Supervised Learning for Ordered Three-Dimensional Structures—0
Efficient Personalized Speech Enhancement through Self-Supervised Learning—0
Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection—0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges—0
Self-Supervised Learning for Segmentation—0
HistoTransfer: Understanding Transfer Learning for Histopathology—0
Distance-Preserving Spatial Representations in Genomic Data—0
Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses—0
HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems—0
Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols—0
Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction—0
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning—0
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning—0
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers—0
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations—0
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems—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