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 6551–6575 of 10307 papers

TitleStatusHype
Robust Authorship Verification with Transfer Learning—0
BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud—0
Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts—0
Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation—0
Robust Deep Sensing Through Transfer Learning in Cognitive Radio—0
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization—0
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset—0
Beyond Fine Tuning: A Modular Approach to Learning on Small Data—0
Robust Few-shot Transfer Learning for Knowledge Base Question Answering with Unanswerable Questions—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Beyond Flatland: Pre-training with a Strong 3D Inductive Bias—0
Robust Generalization of Quadratic Neural Networks via Function Identification—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Machine Learning Applications in Medical Prognostics: A Comprehensive Review—0
Robustifying Sequential Neural Processes—0
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning—0
Robust Indoor Localization in Dynamic Environments: A Multi-source Unsupervised Domain Adaptation Framework—0
Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text—0
Robust Internal Representations for Domain Generalization—0
Learning Robust Data Representation: A Knowledge Flow Perspective—0
Adapted End-to-End Coreference Resolution System for Anaphoric Identities in Dialogues—0
Robust Learning with Frequency Domain Regularization—0
Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques—0
Adapted tree boosting for Transfer Learning—0
Beyond H-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence—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