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 9176–9200 of 10307 papers

TitleStatusHype
DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional NetworksCode0
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain AdaptationCode0
Delta Schema Network in Model-based Reinforcement LearningCode0
Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal DistillationCode0
Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material ClassificationCode0
Depth F_1: Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic GeneralizabilityCode0
Destruction of Image Steganography using Generative Adversarial NetworksCode0
Detached and Interactive Multimodal LearningCode0
Detect, Distill and Update: Detect, Distill and Update: Learned DB Systems Facing Out of Distribution DataCode0
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution DataCode0
Detecting Damage Building Using Real-time Crowdsourced Images and Transfer LearningCode0
Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenariosCode0
Detecting floodwater on roadways from image data with handcrafted features and deep transfer learningCode0
Detection and classification of vocal productions in large scale audio recordingsCode0
Detecting Insincere Questions from Text: A Transfer Learning ApproachCode0
Detecting Text Formality: A Study of Text Classification ApproachesCode0
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource LanguagesCode0
Detection of depression on social networks using transformers and ensemblesCode0
Detection of manatee vocalisations using the Audio Spectrogram TransformerCode0
Detection of Negative Campaign in Israeli Municipal ElectionsCode0
Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based TasksCode0
DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic LandslidesCode0
Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN ArchitecturesCode0
Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning ApproachCode0
Diagnostic Classification Of Lung Nodules Using 3D Neural NetworksCode0
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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