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 8626–8650 of 10307 papers

TitleStatusHype
Personalizing Smartwatch Based Activity Recognition Using Transfer Learning—0
PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer—0
SuMT: A Framework of Summarization and MT—0
Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning—0
Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models—0
PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition—0
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks—0
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data—0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing—0
ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation—0
PGAD: Prototype-Guided Adaptive Distillation for Multi-Modal Learning in AD Diagnosis—0
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning—0
PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning—0
Phase transitions for high-dimensional joint support recovery—0
Phase Transitions in Transfer Learning for High-Dimensional Perceptrons—0
ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising—0
Phonetic Error Analysis of Raw Waveform Acoustic Models with Parametric and Non-Parametric CNNs—0
Phonological Features for Morphological Inflection—0
Phrase-Based Approach for Adaptive Tokenization—0
Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification—0
Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint—0
Physics-Guided Multi-Fidelity DeepONet for Data-Efficient Flow Field Prediction—0
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets—0
Physics-Informed Deep Learning and Partial Transfer Learning for Bearing Fault Diagnosis in the Presence of Highly Missing Data—0
Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Laser Powder Bed Fusion—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