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 876–900 of 10307 papers

TitleStatusHype
Transfer Learning for High-dimensional Quantile Regression with Distribution Shift—0
Towards Santali Linguistic Inclusion: Building the First Santali-to-English Translation Model using mT5 Transformer and Data Augmentation—0
LokiTalk: Learning Fine-Grained and Generalizable Correspondences to Enhance NeRF-based Talking Head Synthesis—0
Knowledge Management for Automobile Failure Analysis Using Graph RAG—0
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling—0
TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action RecognitionCode1
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—0
Parameter-Efficient Transfer Learning for Music Foundation ModelsCode0
Federated Continual Graph LearningCode0
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG—0
Dual Prototyping with Domain and Class Prototypes for Affective Brain-Computer Interface in Unseen Target Conditions—0
Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits—0
Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?—0
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image ClassificationCode1
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications—0
Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation—0
When does a bridge become an aeroplane?—0
Transfer Learning for Deep Learning-based Prediction of Lattice Thermal ConductivityCode0
Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed x-ray radiographyCode0
Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification—0
Breast Tumor Classification Using EfficientNet Deep Learning ModelCode0
Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference—0
Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks—0
Crack Detection in Infrastructure Using Transfer Learning, Spatial Attention, and Genetic Algorithm Optimization—0
On the Generalization of Handwritten Text Recognition Models—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