Calvin
Dataless Model Selection with the Deep Frame Potential
Calvin Murdock, Simon Lucey
Abstract
Choosing a deep neural network architecture is a fundamental problem in applications that require balancing performance and parameter efficiency. Standard approaches rely on ad-hoc engineering or computationally expensive validation on a specific dataset. We instead attempt to quantify networks by their intrinsic capacity for unique and robust representations, enabling efficient architecture comparisons without requiring any data. Building upon theoretical connections between deep learning and sparse approximation, we propose the deep frame potential: a measure of coherence that is approximately related to representation stability but has minimizers that depend only on network structure. This provides a framework for jointly quantifying the contributions of architectural hyper-parameters such as depth, width, and skip connections. We validate its use as a criterion for model selection and demonstrate correlation with generalization error on a variety of common residual and densely connected network architectures.
One paper accepted to CVPR 2020 as an oral presentation!
![]() | Dataless Model Selection with the Deep Frame Potential Conference on Computer Vision and Pattern Recognition (CVPR), 2020, (Oral Presentation). |
One paper accepted to ECCV 2018!
![]() | Deep Component Analysis via Alternating Direction Neural Networks European Conference on Computer Vision (ECCV), 2018. |
New paper posted to arXiv!
Paper
![]() | Deep Component Analysis via Alternating Direction Neural Networks European Conference on Computer Vision (ECCV), 2018. |