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 3651–3675 of 10307 papers

TitleStatusHype
Are We Ready for Out-of-Distribution Detection in Digital Pathology?—0
A Foreground Inference Network for Video Surveillance Using Multi-View Receptive Field—0
ActivityCLIP: Enhancing Group Activity Recognition by Mining Complementary Information from Text to Supplement Image Modality—0
CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Texts—0
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications—0
A Food Photography App with Image Recognition for Thai Food—0
Coherent and Consistent Relational Transfer Learning with Autoencoders—0
Coherence Modeling Improves Implicit Discourse Relation Recognition—0
A Review on Discriminative Self-supervised Learning Methods in Computer Vision—0
Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data—0
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery—0
A Foliated View of Transfer Learning—0
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks—0
Cognitive Learning-Aided Multi-Antenna Communications—0
A review of sentiment analysis research in Arabic language—0
Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture—0
COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning—0
A Review of Deep Transfer Learning and Recent Advancements—0
Coevo: a collaborative design platform with artificial agents—0
A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection—0
Coefficient Shape Transfer Learning for Functional Linear Regression—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
A Survey of Fish Tracking Techniques Based on Computer Vision—0
A Flexible Selection Scheme for Minimum-Effort Transfer Learning—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—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