Learning Versatile Neural Architectures by Propagating Network Codes

• URL: http://arxiv.org/abs/2103.13253v1
• Date: Wed, 24 Mar 2021 15:20:38 GMT
• Title: Learning Versatile Neural Architectures by Propagating Network Codes
• Authors: Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang, Ping Luo
• Abstract summary: We propose a novel "neural predictor", which is able to predict an architecture's performance in multiple datasets and tasks. NCP learns from network codes but not original data, enabling it to update the architecture efficiently across datasets.
• Score: 74.2450894473073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work explores how to design a single neural network that is capable of adapting to multiple heterogeneous tasks of computer vision, such as image segmentation, 3D detection, and video recognition. This goal is challenging because network architecture designs in different tasks are inconsistent. We solve this challenge by proposing Network Coding Propagation (NCP), a novel "neural predictor", which is able to predict an architecture's performance in multiple datasets and tasks. Unlike prior arts of neural architecture search (NAS) that typically focus on a single task, NCP has several unique benefits. (1) NCP can be trained on different NAS benchmarks, such as NAS-Bench-201 and NAS-Bench-MR, which contains a novel network space designed by us for jointly searching an architecture among multiple tasks, including ImageNet, Cityscapes, KITTI, and HMDB51. (2) NCP learns from network codes but not original data, enabling it to update the architecture efficiently across datasets. (3) Extensive experiments evaluate NCP on object classification, detection, segmentation, and video recognition. For example, with 17\% fewer FLOPs, a single architecture returned by NCP achieves 86\% and 77.16\% on ImageNet-50-1000 and Cityscapes respectively, outperforming its counterparts. More interestingly, NCP enables a single architecture applicable to both image segmentation and video recognition, which achieves competitive performance on both HMDB51 and ADE20K compared to the singular counterparts. Code is available at https://github.com/dingmyu/NCP}{https://github.com/dingmyu/NCP.

Related papers

• DNA Family: Boosting Weight-Sharing NAS with Block-Wise Supervisions [121.05720140641189]
  We develop a family of models with the distilling neural architecture (DNA) techniques. Our proposed DNA models can rate all architecture candidates, as opposed to previous works that can only access a sub- search space using algorithms. Our models achieve state-of-the-art top-1 accuracy of 78.9% and 83.6% on ImageNet for a mobile convolutional network and a small vision transformer, respectively.
  arXiv  Detail & Related papers  (2024-03-02T22:16:47Z)
• DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs [53.82853297675979]
  1-bit convolutional neural networks (CNNs) with binary weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS. We introduce Discrepant Child-Parent Neural Architecture Search (DCP-NAS) to efficiently search 1-bit CNNs.
  arXiv  Detail & Related papers  (2023-06-27T11:28:29Z)
• NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks [76.8112416450677]
  Siamese networks are one of the most trending methods to achieve self-supervised visual representation learning (SSL) NASiam is a novel approach that uses for the first time differentiable NAS to improve the multilayer perceptron projector and predictor (encoder/predictor pair) NASiam reaches competitive performance in both small-scale (i.e., CIFAR-10/CIFAR-100) and large-scale (i.e., ImageNet) image classification datasets while costing only a few GPU hours.
  arXiv  Detail & Related papers  (2023-01-31T19:48:37Z)
• Evolutionary Neural Cascade Search across Supernetworks [68.8204255655161]
  We introduce ENCAS - Evolutionary Neural Cascade Search. ENCAS can be used to search over multiple pretrained supernetworks. We test ENCAS on common computer vision benchmarks.
  arXiv  Detail & Related papers  (2022-03-08T11:06:01Z)
• Mixed-Block Neural Architecture Search for Medical Image Segmentation [0.0]
  We propose a novel NAS search space for medical image segmentation networks. It combines the strength of a generalised encoder-decoder structure, well known from U-Net, with network blocks that have proven to have a strong performance in image classification tasks. We find that the networks discovered by our proposed NAS method have better performance than well-known handcrafted segmentation networks.
  arXiv  Detail & Related papers  (2022-02-23T10:32:35Z)
• Hierarchical Neural Architecture Search for Deep Stereo Matching [131.94481111956853]
  We propose the first end-to-end hierarchical NAS framework for deep stereo matching. Our framework incorporates task-specific human knowledge into the neural architecture search framework. It is ranked at the top 1 accuracy on KITTI stereo 2012, 2015 and Middlebury benchmarks, as well as the top 1 on SceneFlow dataset.
  arXiv  Detail & Related papers  (2020-10-26T11:57:37Z)
• FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search [35.61441231491448]
  We propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network. In our experiments, we apply FNA++ on MobileNetV2 to obtain new networks for semantic segmentation, object detection, and human pose estimation. The total computation cost of FNA++ is significantly less than SOTA segmentation and detection NAS approaches.
  arXiv  Detail & Related papers  (2020-06-21T10:03:34Z)
• Neural Architecture Transfer [20.86857986471351]
  Existing approaches require one complete search for each deployment specification of hardware or objective. We propose Neural Architecture Transfer (NAT) to overcome this limitation. NAT is designed to efficiently generate task-specific custom models that are competitive under multiple conflicting objectives.
  arXiv  Detail & Related papers  (2020-05-12T15:30:36Z)
• Fast Neural Network Adaptation via Parameter Remapping and Architecture Search [35.61441231491448]
  Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural network architectures designed for image classification as the backbone. One major challenge though, is that ImageNet pre-training of the search space representation incurs huge computational cost. In this paper, we propose a Fast Neural Network Adaptation (FNA) method, which can adapt both the architecture and parameters of a seed network.
  arXiv  Detail & Related papers  (2020-01-08T13:45:15Z)

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.