Paper Info
Title
Neural Architecture Search of SPD Manifold Networks.
Abstract
In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks. Unlike the conventional NAS problem, our problem requires to search for a unique computational cell called the SPD cell. This SPD cell serves as a basic building block of SPD neural architectures. An efficient solution to our problem is important to minimize the extraneous manual effort in the SPD neural architecture design. To accomplish this goal, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design. Further, we model our new NAS problem using the supernet strategy which models the architecture search problem as a one-shot training process of a single supernet. Based on the supernet modeling, we exploit a differentiable NAS algorithm on our relaxed continuous search space for SPD neural architecture search. Statistical evaluation of our method on drone, action, and emotion recognition tasks mostly provides better results than the state-of-the-art SPD networks and NAS algorithms. Empirical results show that our algorithm excels in discovering better SPD network design, and providing models that are more than 3 times lighter than searched by state-of-the-art NAS algorithms.
Year
DOI
Venue
2021
10.24963/ijcai.2021/413
IJCAI
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Rhea Sanjay Sukthanker100.68
Zhiwu Huang225215.26
Suryansh Kumar3275.53
Erik Goron Endsjo400.34
Yan Wu500.68
Luc Van Gool6275661819.51