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 8151–8175 of 10307 papers

TitleStatusHype
Transfer Learning via _1 Regularization—0
Transfer Learning via Latent Factor Modeling to Improve Prediction of Surgical Complications—0
Transfer Learning via Learning to Transfer—0
Transfer learning via Regularized Linear Discriminant Analysis—0
Transfer Learning via Test-Time Neural Networks Aggregation—0
Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data—0
Transfer Learning with Binary Neural Networks—0
Transfer learning with causal counterfactual reasoning in Decision Transformers—0
Transfer learning with class-weighted and focal loss function for automatic skin cancer classification—0
Transfer Learning with Clinical Concept Embeddings from Large Language Models—0
Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval—0
Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation—0
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray—0
Transfer Learning With Densenet201 Architecture Model For Potato Leaf Disease Classification—0
Transfer Learning with Dynamic Adversarial Adaptation Network—0
Transfer Learning with Dynamic Distribution Adaptation—0
Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets—0
Transfer learning with fewer ImageNet classes—0
Transfer Learning with Foundational Models for Time Series Forecasting using Low-Rank Adaptations—0
Transfer learning with generative models for object detection on limited datasets—0
Estimation and inference for transfer learning with high-dimensional quantile regression—0
Transfer Learning with Human Corneal Tissues: An Analysis of Optimal Cut-Off Layer—0
Transfer Learning with Kernel Methods—0
Transfer Learning with Label Noise—0
Transfer Learning with Neural AutoML—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