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 7351–7375 of 10307 papers

TitleStatusHype
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability—0
The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding—0
10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches—0
Can Machine Translation Bridge Multilingual Pretraining and Cross-lingual Transfer Learning?—0
Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract—0
Transferable Contrastive Network for Generalized Zero-Shot Learning—0
Transferable Cross-Tokamak Disruption Prediction with Deep Hybrid Neural Network Feature Extractor—0
Transferable Curricula through Difficulty Conditioned Generators—0
Transferable Deep Clustering Model—0
Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering—0
Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications—0
Transferable Energy Storage Bidder—0
Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition—0
Transferable Knowledge-Based Multi-Granularity Aggregation Network for Temporal Action Localization: Submission to ActivityNet Challenge 2021—0
Transferable Knowledge for Low-cost Decision Making in Cloud Environments—0
Transferable Neural Processes for Hyperparameter Optimization—0
Transferable Pedestrian Motion Prediction Models at Intersections—0
Transferable Student Performance Modeling for Intelligent Tutoring Systems—0
Transferable Unsupervised Robust Representation Learning—0
Transfer Active Learning For Graph Neural Networks—0
Transfer Alignment Network for Double Blind Unsupervised Domain Adaptation—0
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework—0
Transfer and Multi-Task Learning for Noun--Noun Compound Interpretation—0
Brain MRI Tumor Segmentation with Adversarial Networks—0
Transfer Learning for Causal Effect Estimation—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