In this paper, we propose an efficient NAS algorithm for generating task-specific models that are competitive under multiple competing objectives . We demonstrate the effectiveness and versatility of the proposed method on six diverse non-standard datasets, e.g. STL-10, Flowers102, Oxford Pets, FGVC Aircrafts etc. In all cases, NSGANetV2s improve the state-of-the-art (under mobile setting) suggesting that NAS can be a viable alternative to conventional transfer learning approaches in handling diverse scenarios such as small-scale or fine-grained datasets .

Links: PDF - Abstract

Code :

https://github.com/mikelzc1990/nsganetv2

Keywords : nas - datasets - diverse - nsganetv - -

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