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 2851–2875 of 10307 papers

TitleStatusHype
Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders—0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
Benchmarking Algorithms for Automatic License Plate Recognition—0
Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights—0
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning—0
Democratizing Artificial Intelligence in Healthcare: A Study of Model Development Across Two Institutions Incorporating Transfer Learning—0
Demographic Bias in Presentation Attack Detection of Iris Recognition Systems—0
Demonstration of a Standalone, Descriptive, and Predictive Digital Twin of a Floating Offshore Wind Turbine—0
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction—0
Demystifying BERT: Implications for Accelerator Design—0
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan—0
Deep Learning for Apple Diseases: Classification and Identification—0
De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks—0
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays—0
Benchmarking Image Embeddings for E-Commerce: Evaluating Off-the Shelf Foundation Models, Fine-Tuning Strategies and Practical Trade-offs—0
Dense Classification and Implanting for Few-Shot Learning—0
Analysis of three dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis—0
Domain Adaptive Unfolded Graph Neural Networks—0
Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection—0
Deep learning for affective computing: text-based emotion recognition in decision support—0
Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera—0
Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach—0
Dependency Parsing: Past, Present, and Future—0
Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement 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