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 2371–2380 of 10307 papers

TitleStatusHype
BERT-Based Approach for Automating Course Articulation Matrix Construction with Explainable AICode0
Revised Regularization for Efficient Continual Learning through Correlation-Based Parameter Update in Bayesian Neural Networks—0
SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation—0
Transforming Static Images Using Generative Models for Video Salient Object Detection—0
MindForge: Empowering Embodied Agents with Theory of Mind for Lifelong Collaborative Learning—0
Prior-based Objective Inference Mining Potential Uncertainty for Facial Expression RecognitionCode0
Effective Analog ICs Floorplanning with Relational Graph Neural Networks and Reinforcement Learning—0
Domain Adaptive Unfolded Graph Neural Networks—0
Multivariate and Online Transfer Learning with Uncertainty Quantification—0
Classification of Geographical Land Structure Using Convolution Neural Network and Transfer Learning—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