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 5001–5025 of 10307 papers

TitleStatusHype
Palm Vein Recognition via Multi-task Loss Function and Attention Layer—0
Assistive Completion of Agrammatic Aphasic Sentences: A Transfer Learning Approach using Neurolinguistics-based Synthetic Dataset—0
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication—0
Towards Global Crop Maps with Transfer Learning—0
Cross-lingual Transfer Learning for Check-worthy Claim Identification over Twitter—0
Framework Construction of an Adversarial Federated Transfer Learning Classifier—0
Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images—0
From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model—0
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI AcceleratorsCode0
Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing—0
When & How to Transfer with Transfer LearningCode0
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes—0
Variational Quantum Kernels with Task-Specific Quantum Metric Learning—0
Understanding the Role of Mixup in Knowledge Distillation: An Empirical StudyCode0
A Semiparametric Efficient Approach To Label Shift Estimation and Quantification—0
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain RecommendationsCode0
Rotation-equivariant Graph Neural Networks for Learning Glassy Liquids Representations—0
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments—0
1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems—0
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments—0
Integrated Parameter-Efficient Tuning for General-Purpose Audio ModelsCode0
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion—0
Rethinking the transfer learning for FCN based polyp segmentation in colonoscopyCode0
Unsupervised Visual Representation Learning via Mutual Information Regularized AssignmentCode0
Overcoming Barriers to Skill Injection in Language Modeling: Case Study in ArithmeticCode0
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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