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 4351–4375 of 10307 papers

TitleStatusHype
A Meta-Learning Approach to Population-Based Modelling of Structures—0
MaLTESE: Large-Scale Simulation-Driven Machine Learning for Transient Driving Cycles—0
Cross-user activity recognition via temporal relation optimal transport—0
Cross-Task Representation Learning for Anatomical Landmark Detection—0
A Two stage Adaptive Knowledge Transfer Evolutionary Multi-tasking Based on Population Distribution for Multi/Many-Objective Optimization—0
Cross-Task Knowledge Transfer for Visually-Grounded Navigation—0
Cross-Task Knowledge Transfer for Query-Based Text Summarization—0
A Meta-Learning Approach for Few-Shot (Dis)Agreement Identification in Online Discussions—0
A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data—0
Exploring Optimal Deep Learning Models for Image-based Malware Variant Classification—0
Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes—0
Cross-Task Knowledge Distillation in Multi-Task Recommendation—0
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks—0
Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces—0
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface—0
Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning—0
A Meta-Learning Approach for Custom Model Training—0
Cross-subject Decoding of Eye Movement Goals from Local Field Potentials—0
Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning—0
Attr2Style: A Transfer Learning Approach for Inferring Fashion Styles via Apparel Attributes—0
A Medical Pre-Diagnosis System for Histopathological Image of Breast Cancer—0
Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation—0
Hierarchical Side-Tuning for Vision Transformers—0
Cross-position Activity Recognition with Stratified Transfer Learning—0
Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans—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