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This work introduces improvements to the stability and generalizability ofCyclic DARTS (CDARTS). CDARTS is a Differentiable Architecture Search(DARTS)-based approach to neural architecture search (NAS) that uses a cyclicfeedback mechanism to train search and evaluation networks concurrently. Thistraining protocol aims to optimize the search process by enforcing that thesearch and evaluation networks produce similar outputs. However, CDARTSintroduces a loss function for the evaluation network that is dependent on thesearch network. The dissimilarity between the loss functions used by theevaluation networks during the search and retraining phases results in asearch-phase evaluation network that is a sub-optimal proxy for the finalevaluation network that is utilized during retraining. We present ICDARTS, arevised approach that eliminates the dependency of the evaluation networkweights upon those of the search network, along with a modified process fordiscretizing the search network's \textit{zero} operations that allows theseoperations to be retained in the final evaluation networks. We pair the resultsof these changes with ablation studies on ICDARTS' algorithm and networktemplate. Finally, we explore methods for expanding the search space of ICDARTSby expanding its operation set and exploring alternate methods for discretizingits continuous search cells. These experiments resulted in networks withimproved generalizability and the implementation of a novel method forincorporating a dynamic search space into ICDARTS.

ICDARTS: Improving the Stability and Performance of Cyclic DARTS