Hard Sample Matters a Lot in Zero-Shot Quantization

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samples. Nonetheless, we find that the synthetic samples constructed in existing ZSQ methods can be easily fitted by models. Accordingly, quantized models obtained by these methods suffer from significant performance degradation on hard samples. To address this issue, we propose HArd sample Synthesizing and Training (HAST). Specifically, HAST pays more attention to hard samples when synthesizing samples and makes synthetic samples hard to fit when training quantized models. HAST aligns features extracted by full-precision and quantized models to ensure the similarity between features extracted by these two models. Extensive experiments show that HAST significantly outperforms existing ZSQ methods, achieving performance comparable to models that are quantized with real data.

Automatic Subspace Evoking for Efficient Neural Architecture Search

Neural Architecture Search (NAS) aims to automatically find effective architectures from a predefined search space. However, the search space is often extremely large. As a result, directly searching in such a large search space is non-trivial and also very time-consuming. To address the above issues, in each search step, we seek to limit the search space to a small but effective subspace to boost both the search performance and search efficiency. To this end, we propose a novel Neural Architecture Search method via Automatic Subspace Evoking (ASE-NAS) that finds promising architectures in automatically evoked subspaces. Specifically, we first perform a global search, i.e., automatic subspace evoking, to evoke/find a good subspace from a set of candidates. Then, we perform a local search within the evoked subspace to find an effective architecture. More critically, we further boost search performance by taking well-designed/searched architectures as the initial candidate subspaces. Extensive experiments show that our ASE-NAS not only greatly reduces the search cost but also finds better architectures than state-of-the-art methods in various benchmark search spaces.