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 3776–3800 of 10307 papers

TitleStatusHype
Exploring Knowledge Transfer in Evolutionary Many-task Optimization: A Complex Network Perspective—0
CloudifierNet -- Deep Vision Models for Artificial Image Processing—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
Exploring Multi-Level Threats in Telegram Data with AI-Human Annotation: A Preliminary Study—0
ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data—0
Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos—0
EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation—0
Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding—0
Close Yet Distinctive Domain Adaptation—0
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities—0
Examining the behaviour of state-of-the-art convolutional neural networks for brain tumor detection with and without transfer learning—0
EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning—0
Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection—0
A Feature Extraction based Model for Hate Speech Identification—0
Exploring Region-Word Alignment in Built-in Detector for Open-Vocabulary Object Detection—0
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration—0
Evolving Image Compositions for Feature Representation Learning—0
Evolution of transfer learning in natural language processing—0
Evolution of ReID: From Early Methods to LLM Integration—0
Exploring Task Unification in Graph Representation Learning via Generative Approach—0
Evolutionary Multitasking with Solution Space Cutting for Point Cloud Registration—0
A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction—0
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains—0
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning—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