7 citations · 36 across the 19 of their papers we have counts for
3 papers · 1 filter
DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Fai…
Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition
Nikolaos Louloudakis, Perry Gibson, José Cano +1
When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is erro…
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
Xin Du, Benedicte Legastelois, Bhargavi Ganesh +5
Using large pre-trained models for image recognition tasks is becoming increasingly common owing to the well acknowledged success of recent models like vision transformers and othe…