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 9401–9425 of 10307 papers

TitleStatusHype
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering—0
Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources—0
CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning—0
Bi-level Unbalanced Optimal Transport for Partial Domain Adaptation—0
Contextual Transformation Networks for Online Continual Learning—0
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans—0
Real-time detection of uncalibrated sensors using Neural Networks—0
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—0
Continual Few-shot Intent Detection—0
Continual Learning for Anomaly Detection in Surveillance Videos—0
Continual Learning for Tumor Classification in Histopathology Images—0
Continual Learning in the Presence of Spurious Correlation—0
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
BIGSAGE: unsupervised inductive representation learning of graph via bi-attended sampling and global-biased aggregating—0
Continual Learning of Natural Language Processing Tasks: A Survey—0
Continual Learning on the Edge with TensorFlow Lite—0
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation—0
Continual Learning with Adaptive Weights (CLAW)—0
Continual Learning with Dirichlet Generative-based Rehearsal—0
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation—0
Bidirectional Language Models Are Also Few-shot Learners—0
Continual Lifelong Learning with Neural Networks: A Review—0
Continually Detection, Rapidly React: Unseen Rumors Detection Based on Continual Prompt-Tuning—0
Towards continually learning new languages—0
Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks—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