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Poster
Theory-Inspired Path-Regularized Differential Network Architecture Search
Pan Zhou · Caiming Xiong · Richard Socher · Steven Chu Hong Hoi

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1771
in Poster Session 7 »

Despite its high search efficiency, differential architecture search (DARTS) often selects network architectures with dominated skip connections which lead to performance degradation. However, theoretical understandings on this issue remain absent, hindering the development of more advanced methods in a principled way. In this work, we solve this problem by theoretically analyzing the effects of various types of operations, e.g. convolution, skip connection and zero operation, to the network optimization. We prove that the architectures with more skip connections can converge faster than the other candidates, and thus are selected by DARTS. This result, for the first time, theoretically and explicitly reveals the impact of skip connections to fast network optimization and its competitive advantage over other types of operations in DARTS. Then we propose a theory-inspired path-regularized DARTS that consists of two key modules: (i) a differential group-structured sparse binary gate introduced for each operation to avoid unfair competition among operations, and (ii) a path-depth-wise regularization used to incite search exploration for deep architectures that often converge slower than shallow ones as shown in our theory and are not well explored during search. Experimental results on image classification tasks validate its advantages. Codes and models will be released.

Author Information

Pan Zhou (Salesforce)
Caiming Xiong (Salesforce)
Richard Socher (Salesforce)

Richard Socher is Chief Scientist at Salesforce. He leads the company’s research efforts and brings state of the art artificial intelligence solutions into the platform. Prior, Richard was an adjunct professor at the Stanford Computer Science Department and the CEO and founder of MetaMind, a startup acquired by Salesforce in April 2016. MetaMind’s deep learning AI platform analyzes, labels and makes predictions on image and text data so businesses can make smarter, faster and more accurate decisions.

Steven Chu Hong Hoi (Salesforce)

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