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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4251–4275 of 10307 papers

TitleStatusHype
GDA-HIN: A Generalized Domain Adaptive Model across Heterogeneous Information Networks—0
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation—0
Amobee at IEST 2018: Transfer Learning from Language Models—0
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Hard instance learning for quantum adiabatic prime factorization—0
DADNN: Multi-Scene CTR Prediction via Domain-Aware Deep Neural Network—0
DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems—0
DA-DETR: Domain Adaptive Detection Transformer with Information Fusion—0
A Unified Framework for Cross-Domain Recommendation—0
Adaptive Physics-informed Neural Networks: A Survey—0
Guided Recommendation for Model Fine-Tuning—0
A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments—0
DACB-Net: Dual Attention Guided Compact Bilinear Convolution Neural Network for Skin Disease Classification—0
A Unified Deep Learning Approach for Prediction of Parkinson's Disease—0
D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation—0
10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches—0
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning—0
CyFormer: Accurate State-of-Health Prediction of Lithium-Ion Batteries via Cyclic Attention—0
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction—0
Supervised domain adaptation for building extraction from off-nadir aerial images—0
AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models—0
GTA: Guided Transfer of Spatial Attention from Object-Centric Representations—0
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data—0
Cyclegan Network for Sheet Metal Welding Drawing Translation—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