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 7851–7875 of 10307 papers

TitleStatusHype
Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation—0
ELSIM: End-to-end learning of reusable skills through intrinsic motivation—0
Advantages of biologically-inspired adaptive neural activation in RNNs during learning—0
Convolutional-network models to predict wall-bounded turbulence from wall quantities—0
Generalized Zero and Few-Shot Transfer for Facial Forgery Detection—0
Learning compact generalizable neural representations supporting perceptual grouping—0
Adversarial Transfer of Pose Estimation Regression—0
On the Theory of Transfer Learning: The Importance of Task Diversity—0
Unsupervised Image Classification for Deep Representation LearningCode0
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms—0
Unified Representation Learning for Efficient Medical Image Analysis—0
BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud—0
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?Code0
New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles—0
Delta Schema Network in Model-based Reinforcement LearningCode0
Learning a functional control for high-frequency finance—0
Deep Categorization with Semi-Supervised Self-Organizing MapsCode0
Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated TasksCode0
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural NetworksCode0
Response by the Montreal AI Ethics Institute to the European Commission's Whitepaper on AI—0
Domain Adaptation with Joint Learning for Generic, Optical Car Part Recognition and Detection Systems (Go-CaRD)—0
Using Mobility for Electrical Load Forecasting During the COVID-19 PandemicCode0
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya—0
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks—0
Distant Transfer Learning via Deep Random Walk—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