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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8226–8250 of 10307 papers

TitleStatusHype
Object Detection in Indian Food Platters using Transfer Learning with YOLOv4—0
Object Detection Using Deep CNNs Trained on Synthetic Images—0
Object Detection Using Sim2Real Domain Randomization for Robotic Applications—0
The Evolution and Future Perspectives of Artificial Intelligence Generated Content—0
Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection—0
Object Localization with a Weakly Supervised CapsNet—0
Object Recognition from very few Training Examples for Enhancing Bicycle Maps—0
Object Tracking Incorporating Transfer Learning into Unscented and Cubature Kalman Filters—0
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
ODGR: Online Dynamic Goal Recognition—0
StEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic—0
Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning—0
A general method for regularizing tensor decomposition methods via pseudo-data—0
Offensive Language Detection on Video Live Streaming Chat—0
Offensive language identification in Dravidian code mixed social media text—0
Offensive Text Detection Across Languages and Datasets Using Rule-based and Hybrid Methods—0
Offensive Video Detection: Dataset and Baseline Results—0
Offline Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach—0
Offline-to-online hyperparameter transfer for stochastic bandits—0
A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks—0
A General Class of Transfer Learning Regression without Implementation Cost—0
Steerable Equivariant Representation Learning—0
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions—0
OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation—0
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System—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