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 1976–2000 of 10307 papers

TitleStatusHype
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition—0
Transfer Learning for Cross-dataset Isolated Sign Language Recognition in Under-Resourced DatasetsCode0
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic EncryptionCode1
Text-Enhanced Data-free Approach for Federated Class-Incremental LearningCode1
Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets—0
Stitching for Neuroevolution: Recombining Deep Neural Networks without Breaking Them—0
Learning to Project for Cross-Task Knowledge Distillation—0
A Transfer Learning Causal Approach to Evaluate Racial/Ethnic and Geographic Variation in Outcomes Following Congenital Heart Surgery—0
Exploring Task Unification in Graph Representation Learning via Generative Approach—0
Diffusion-based Human Motion Style Transfer with Semantic Guidance—0
Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems—0
Learning from Synthetic Data for Visual Grounding—0
AdaTrans: Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Regression—0
Arcee's MergeKit: A Toolkit for Merging Large Language ModelsCode9
Progressive trajectory matching for medical dataset distillation—0
FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image AnalysisCode0
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning—0
A fast general thermal simulation model based on MultiBranch Physics-Informed deep operator neural network—0
TransformMix: Learning Transformation and Mixing Strategies from Data—0
Wildfire danger prediction optimization with transfer learningCode0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning—0
Semantics, Distortion, and Style Matter: Towards Source-free UDA for Panoramic Segmentation—0
Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer LearningCode0
Removing Undesirable Concepts in Text-to-Image Diffusion Models with Learnable PromptsCode1
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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