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 1676–1700 of 10307 papers

TitleStatusHype
Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders—0
Confidence Aware Neural Networks for Skin Cancer Detection—0
Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need—0
A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imaging—0
A Feature Extraction based Model for Hate Speech Identification—0
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration—0
Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior—0
A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction—0
A QUBO Framework for Team Formation—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variables—0
A fast general thermal simulation model based on MultiBranch Physics-Informed deep operator neural network—0
Accelerating Multi-Model Inference by Merging DNNs of Different Weights—0
CON: Continual Object Navigation via Data-Free Inter-Agent Knowledge Transfer in Unseen and Unfamiliar Places—0
Active Multitask Learning with Committees—0
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning—0
Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT—0
A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition—0
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
A probabilistic constrained clustering for transfer learning and image category discovery—0
Active Multitask Learning with Committees—0
Conceptual Expansion Neural Architecture Search (CENAS)—0
Concrete Surface Crack Detection with Convolutional-based Deep Learning Models—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