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 4626–4650 of 10307 papers

TitleStatusHype
NRC-CNRC Systems for Upper Sorbian-German and Lower Sorbian-German Machine Translation 2021—0
NRC Systems for Low Resource German-Upper Sorbian Machine Translation 2020: Transfer Learning with Lexical Modifications—0
NSCGCN: A novel deep GCN model to diagnosis COVID-19—0
NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning—0
NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation—0
NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains—0
NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers—0
Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks—0
NUMSnet: Nested-U Multi-class Segmentation network for 3D Medical Image Stacks—0
Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin—0
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes—0
Object detection-based inspection of power line insulators: Incipient fault detection in the low data-regime—0
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
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
ODGR: Online Dynamic Goal Recognition—0
Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning—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
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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