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 3876–3900 of 10307 papers

TitleStatusHype
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types—0
FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing—0
Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification—0
Decoupled classifiers for fair and efficient machine learning—0
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering—0
Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models—0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
Fake news detection using parallel BERT deep neural networks—0
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces—0
Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information—0
A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning—0
Using LLMs to Establish Implicit User Sentiment of Software Desirability—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
Fast Adaptation with Linearized Neural Networks—0
Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—0
Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents—0
Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
Decomposable Probability-of-Success Metrics in Algorithmic Search—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
Fast Data-Driven Adaptation of Radar Detection via Meta-Learning—0
Adaptive Transfer Learning for Plant Phenotyping—0
Fine-Tuning Models Comparisons on Garbage Classification for Recyclability—0
Show:102550
← PrevPage 156 of 413Next →
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