Discretization-Aware Architecture Search

• URL: http://arxiv.org/abs/2007.03154v1
• Date: Tue, 7 Jul 2020 01:18:58 GMT
• Title: Discretization-Aware Architecture Search
• Authors: Yunjie Tian, Chang Liu, Lingxi Xie, Jianbin Jiao, Qixiang Ye
• Abstract summary: This paper presents discretization-aware architecture search (DAtextsuperscript2S) The core idea is to push the super-network towards the configuration of desired topology, so that the accuracy loss brought by discretization is largely alleviated. Experiments on standard image classification benchmarks demonstrate the superiority of our approach.
• Score: 81.35557425784026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods. These methods optimize a super-network with all possible edges and operations, and determine the optimal sub-network by discretization, \textit{i.e.}, pruning off weak candidates. The discretization process, performed on either operations or edges, incurs significant inaccuracy and thus the quality of the final architecture is not guaranteed. This paper presents discretization-aware architecture search (DA\textsuperscript{2}S), with the core idea being adding a loss term to push the super-network towards the configuration of desired topology, so that the accuracy loss brought by discretization is largely alleviated. Experiments on standard image classification benchmarks demonstrate the superiority of our approach, in particular, under imbalanced target network configurations that were not studied before.

Related papers

• Network Pruning Spaces [12.692532576302426]
  Network pruning techniques, including weight pruning and filter pruning, reveal that most state-of-the-art neural networks can be accelerated without a significant performance drop. This work focuses on filter pruning which enables accelerated inference with any off-the-shelf deep learning library and hardware.
  arXiv  Detail & Related papers  (2023-04-19T06:52:05Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• Pushing the Efficiency Limit Using Structured Sparse Convolutions [82.31130122200578]
  We propose Structured Sparse Convolution (SSC), which leverages the inherent structure in images to reduce the parameters in the convolutional filter. We show that SSC is a generalization of commonly used layers (depthwise, groupwise and pointwise convolution) in efficient architectures'' Architectures based on SSC achieve state-of-the-art performance compared to baselines on CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet classification benchmarks.
  arXiv  Detail & Related papers  (2022-10-23T18:37:22Z)
• Efficient Joint-Dimensional Search with Solution Space Regularization for Real-Time Semantic Segmentation [27.94898516315886]
  We search an optimal network structure that can run in real-time for this problem. A novel Solution Space Regularization (SSR) loss is first proposed to effectively encourage the supernet to converge to its discrete one. A new Hierarchical and Progressive Solution Space Shrinking method is presented to further achieve high efficiency of searching.
  arXiv  Detail & Related papers  (2022-08-10T11:07:33Z)
• Generalizing Few-Shot NAS with Gradient Matching [165.5690495295074]
  One-Shot methods train one supernet to approximate the performance of every architecture in the search space via weight-sharing. Few-Shot NAS reduces the level of weight-sharing by splitting the One-Shot supernet into multiple separated sub-supernets. It significantly outperforms its Few-Shot counterparts while surpassing previous comparable methods in terms of the accuracy of derived architectures.
  arXiv  Detail & Related papers  (2022-03-29T03:06:16Z)
• Weight Reparametrization for Budget-Aware Network Pruning [12.633386045916444]
  We introduce an "end-to-end" lightweight network design that achieves training and pruning simultaneously without fine-tuning. Our method relies on reparametrization that learns not only the weights but also the topological structure of the lightweight sub-network. Experiments conducted on the CIFAR10 and the TinyImageNet datasets, using standard architectures, show compelling results without fine-tuning.
  arXiv  Detail & Related papers  (2021-07-08T15:40:16Z)
• AP-Loss for Accurate One-Stage Object Detection [49.13608882885456]
  One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously. The former suffers much from extreme foreground-background imbalance due to the large number of anchors. This paper proposes a novel framework to replace the classification task in one-stage detectors with a ranking task.
  arXiv  Detail & Related papers  (2020-08-17T13:22:01Z)
• Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks [100.14670789581811]
  We train a graph convolutional network to fit the performance of sampled sub-networks. With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates.
  arXiv  Detail & Related papers  (2020-04-17T19:12:39Z)

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.