DistilNAS: Neural Architecture Search With Distilled Data

Can we perform Neural Architecture Search (NAS) with a smaller subset of target dataset and still fair better in terms of performance with significant reduction in search cost? In this work, we propose a method, called DistilNAS, which utilizes a curriculum learning based approach to distill the target dataset into a very efficient smaller dataset to perform NAS. We hypothesize that only the data samples containing features highly relevant to a given class should be used in the search phase of the NAS. We perform NAS with a distilled version of dataset and the searched model achieves a better performance with a much reduced search cost in comparison with various baselines. For instance, on Imagenet dataset, the DistilNAS uses only 10% of the training data and produces a model in ≈1 GPU-day (includes the time needed for clustering) that achieves near SOTA accuracy of 75.75% (PC-DARTS had achieved SOTA with an accuracy of 75.8% but needed 3.8 GPU-days for architecture search). We also demonstrate and discuss the efficacy of DistilNAS on several other publicly available datasets.