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 6026–6050 of 10307 papers

TitleStatusHype
Enhanced dynamic sign language recognition using slowfast networks—0
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers—0
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation—0
PowerGraph: Using neural networks and principal components to determine multivariate statistical power trade-offs—0
A transfer learning enhanced the physics-informed neural network model for vortex-induced vibration—0
Feature matching as improved transfer learning technique for wearable EEG—0
Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models—0
Cross-Domain Open-Set Machinery Fault Diagnosis Based on Adversarial Network With Multiple Auxiliary Classifiers—0
Delivery Issues Identification from Customer Feedback Data—0
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence—0
Spoiler in a Textstack: How Much Can Transformers Help?—0
Faster Deep Ensemble Averaging for Quantification of DNA Damage from Comet Assay Images With Uncertainty Estimates—0
Dual Path Structural Contrastive Embeddings for Learning Novel Objects—0
Distilling the Knowledge of Romanian BERTs Using Multiple TeachersCode0
Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text SummarizationCode0
Toward Educator-focused Automated Scoring Systems for Reading and Writing—0
Data-Driven Outage Restoration Time Prediction via Transfer Learning with Cluster Ensembles—0
The Changing Role of Entrepreneurial Universities in the Altering Innovation Policy: Opportunities Arising from the Paradigm Change in Light of the Experience of Széchenyi István University—0
FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence—0
Evaluation and Comparison of Deep Learning Methods for Pavement Crack Identification with Visual ImagesCode0
A Vision-based Solution for Track Misalignment Detection—0
Rethinking Importance Weighting for Transfer Learning—0
TECM: Transfer Learning-based Evidential C-Means Clustering—0
Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource LanguagesCode0
AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data—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