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 2326–2350 of 10307 papers

TitleStatusHype
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
Transfer Learning for High-dimensional Quantile Regression with Distribution Shift—0
Parameter-Efficient Transfer Learning for Music Foundation ModelsCode0
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG—0
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling—0
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—0
Federated Continual Graph LearningCode0
Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification—0
Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?—0
When does a bridge become an aeroplane?—0
Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits—0
Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed x-ray radiographyCode0
Transfer Learning for Deep Learning-based Prediction of Lattice Thermal ConductivityCode0
Dual Prototyping with Domain and Class Prototypes for Affective Brain-Computer Interface in Unseen Target Conditions—0
Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation—0
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications—0
On the Generalization of Handwritten Text Recognition Models—0
Multimodal Alignment and Fusion: A Survey—0
Breast Tumor Classification Using EfficientNet Deep Learning ModelCode0
Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks—0
Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference—0
Crack Detection in Infrastructure Using Transfer Learning, Spatial Attention, and Genetic Algorithm Optimization—0
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data GenerationCode0
Towards Foundation Models for Critical Care Time Series—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