Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization

• URL: http://arxiv.org/abs/2206.01198v1
• Date: Thu, 2 Jun 2022 17:58:54 GMT
• Title: Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization
• Authors: Yanyu Li, Pu Zhao, Geng Yuan, Xue Lin, Yanzhi Wang, Xin Chen
• Abstract summary: Pruning-as-Search (PaS) is an end-to-end channel pruning method to search out desired sub-network automatically and efficiently. Our proposed architecture outperforms prior arts by around $1.0%$ top-1 accuracy on ImageNet-1000 classification task.
• Score: 50.50023451369742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural architecture search (NAS) and network pruning are widely studied efficient AI techniques, but not yet perfect. NAS performs exhaustive candidate architecture search, incurring tremendous search cost. Though (structured) pruning can simply shrink model dimension, it remains unclear how to decide the per-layer sparsity automatically and optimally. In this work, we revisit the problem of layer-width optimization and propose Pruning-as-Search (PaS), an end-to-end channel pruning method to search out desired sub-network automatically and efficiently. Specifically, we add a depth-wise binary convolution to learn pruning policies directly through gradient descent. By combining the structural reparameterization and PaS, we successfully searched out a new family of VGG-like and lightweight networks, which enable the flexibility of arbitrary width with respect to each layer instead of each stage. Experimental results show that our proposed architecture outperforms prior arts by around $1.0\%$ top-1 accuracy under similar inference speed on ImageNet-1000 classification task. Furthermore, we demonstrate the effectiveness of our width search on complex tasks including instance segmentation and image translation. Code and models are released.

Related papers

• Tiered Pruning for Efficient Differentialble Inference-Aware Neural Architecture Search [0.0]
  We introduce, a bi-path building block for DNAS, which can search over inner hidden dimensions with memory and compute complexity. Second, we present an algorithm for pruning blocks within a layer of the SuperNet during the search. Third, we describe a novel technique for pruning unnecessary layers during the search.
  arXiv  Detail & Related papers  (2022-09-23T18:03:54Z)
• DAAS: Differentiable Architecture and Augmentation Policy Search [107.53318939844422]
  This work considers the possible coupling between neural architectures and data augmentation and proposes an effective algorithm jointly searching for them. Our approach achieves 97.91% accuracy on CIFAR-10 and 76.6% Top-1 accuracy on ImageNet dataset, showing the outstanding performance of our search algorithm.
  arXiv  Detail & Related papers  (2021-09-30T17:15:17Z)
• Enhanced Gradient for Differentiable Architecture Search [17.431144144044968]
  We propose a neural network architecture search algorithm aiming to simultaneously improve network performance and reduce network complexity. The proposed framework automatically builds the network architecture at two stages: block-level search and network-level search. Experiment results demonstrate that our method outperforms all evaluated hand-crafted networks in image classification.
  arXiv  Detail & Related papers  (2021-03-23T13:27:24Z)
• ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding [86.40042104698792]
  We formulate neural architecture search as a sparse coding problem. In experiments, our two-stage method on CIFAR-10 requires only 0.05 GPU-day for search. Our one-stage method produces state-of-the-art performances on both CIFAR-10 and ImageNet at the cost of only evaluation time.
  arXiv  Detail & Related papers  (2020-10-13T04:34:24Z)
• DrNAS: Dirichlet Neural Architecture Search [88.56953713817545]
  We treat the continuously relaxed architecture mixing weight as random variables, modeled by Dirichlet distribution. With recently developed pathwise derivatives, the Dirichlet parameters can be easily optimized with gradient-based generalization. To alleviate the large memory consumption of differentiable NAS, we propose a simple yet effective progressive learning scheme.
  arXiv  Detail & Related papers  (2020-06-18T08:23:02Z)
• DC-NAS: Divide-and-Conquer Neural Architecture Search [108.57785531758076]
  We present a divide-and-conquer (DC) approach to effectively and efficiently search deep neural architectures. We achieve a $75.1%$ top-1 accuracy on the ImageNet dataset, which is higher than that of state-of-the-art methods using the same search space.
  arXiv  Detail & Related papers  (2020-05-29T09:02:16Z)
• DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning [135.27931587381596]
  We propose an efficient and unified NAS framework termed DDPNAS via dynamic distribution pruning. In particular, we first sample architectures from a joint categorical distribution. Then the search space is dynamically pruned and its distribution is updated every few epochs. With the proposed efficient network generation method, we directly obtain the optimal neural architectures on given constraints.
  arXiv  Detail & Related papers  (2019-05-28T06:35:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.