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 9026–9050 of 10307 papers

TitleStatusHype
Context Matters: Leveraging Spatiotemporal Metadata for Semi-Supervised Learning on Remote Sensing ImagesCode0
Contextual Dialogue Act Classification for Open-Domain Conversational AgentsCode0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Continual Dialogue State Tracking via Reason-of-Select DistillationCode0
Continual Prune-and-Select: Class-incremental learning with specialized subnetworksCode0
Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer LearningCode0
Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-TuningCode0
Contrastive Bi-Projector for Unsupervised Domain AdaptionCode0
Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge TransferCode0
CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI ReconstructionCode0
Contrastive learning of T cell receptor representationsCode0
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-RaysCode0
Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary LearningCode0
Conversational AI for Positive-sum Retailing under Falsehood ControlCode0
Convolutional neural networks for Alzheimer’s disease detection on MRI imagesCode0
Automated Classification of Histopathology Images Using Transfer LearningCode0
Cooperative Knowledge Distillation: A Learner Agnostic ApproachCode0
Copy mechanism and tailored training for character-based data-to-text generationCode0
Coreference Resolution in Research Papers from Multiple DomainsCode0
CoRe-Net: Co-Operational Regressor Network with Progressive Transfer Learning for Blind Radar Signal RestorationCode0
Corona-Nidaan: lightweight deep convolutional neural network for chest X-Ray based COVID-19 infection detectionCode0
Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channelsCode0
Correlational Neural NetworksCode0
Correlation Congruence for Knowledge DistillationCode0
Corresponding Projections for Orphan ScreeningCode0
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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