ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation

• URL: http://arxiv.org/abs/2104.06957v1
• Date: Wed, 14 Apr 2021 16:33:48 GMT
• Title: ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation
• Authors: Martin Ferianc, Divyansh Manocha, Hongxiang Fan, Miguel Rodrigues
• Abstract summary: We tackle two defects that hinder convolutional neural networks in real-world applications. We demonstrate a few- parameters compact Bayesian convolutional architecture, that achieves a marginal improvement in accuracy. The architecture combines parameter-efficient operations such as separable convolutions, bi-pixel, multi-scale feature propagation and Bayesian inference for per- uncertainty quantification through Monte Carlo Dropout.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Fully convolutional U-shaped neural networks have largely been the dominant approach for pixel-wise image segmentation. In this work, we tackle two defects that hinder their deployment in real-world applications: 1) Predictions lack uncertainty quantification that may be crucial to many decision making systems; 2) Large memory storage and computational consumption demanding extensive hardware resources. To address these issues and improve their practicality we demonstrate a few-parameter compact Bayesian convolutional architecture, that achieves a marginal improvement in accuracy in comparison to related work using significantly fewer parameters and compute operations. The architecture combines parameter-efficient operations such as separable convolutions, bi-linear interpolation, multi-scale feature propagation and Bayesian inference for per-pixel uncertainty quantification through Monte Carlo Dropout. The best performing configurations required fewer than 2.5 million parameters on diverse challenging datasets with few observations.

Related papers

• SlimEdge: Lightweight Distributed DNN Deployment on Constrained Hardware [0.6219985442687116]
  Deep distributed networks (DNNs) have become central to modern computer vision.<n>Our method integrates a structured model pruning with a multi-objective optimization to tailor network capacity to heterogeneous device constraints.<n>Results show that the resulting models satisfy user-specified bounds on accuracy and memory footprint while reducing inference latency by factors ranging from 1.2x to 5.0x.
  arXiv  Detail & Related papers  (2025-12-11T04:02:21Z)
• MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation [13.137436418148896]
  Ground-based cloud image segmentation is a critical research domain for photovoltaic power forecasting.<n>We propose MPCM-Net, a Multi-scale network that integrates Partial attention Convolutions with Mamba architectures to enhance segmentation accuracy and computational efficiency.<n>As a key contribution to the community, we also introduce and release a dataset CSRC, which is a clear-label, fine-grained segmentation benchmark designed to overcome the critical limitations of existing public datasets.
  arXiv  Detail & Related papers  (2025-11-12T06:17:49Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks [7.0710630443004705]
  Inferring exact parameters of a neural network with only query access is an NP-Hard problem. We present a novel query generation algorithm that produces maximally informative samples, letting us untangle the non-linear relationships efficiently. We demonstrate reconstruction of a hidden network containing over 1.5 million parameters, and of one 7 layers deep, the largest and deepest reconstructions to date, with max parameter difference less than 0.0001.
  arXiv  Detail & Related papers  (2024-09-27T21:02:04Z)
• Parameter-Inverted Image Pyramid Networks [49.35689698870247]
  We propose a novel network architecture known as the Inverted Image Pyramid Networks (PIIP) Our core idea is to use models with different parameter sizes to process different resolution levels of the image pyramid. PIIP achieves superior performance in tasks such as object detection, segmentation, and image classification.
  arXiv  Detail & Related papers  (2024-06-06T17:59:10Z)
• SySMOL: Co-designing Algorithms and Hardware for Neural Networks with Heterogeneous Precisions [20.241671088121144]
  Recent quantization techniques have enabled heterogeneous precisions at very fine granularity. These networks require additional hardware to decode the precision settings for individual variables, align the variables, and provide fine-grained mixed-precision compute capabilities. We present an end-to-end co-design approach to efficiently execute networks with fine-grained heterogeneous precisions.
  arXiv  Detail & Related papers  (2023-11-23T17:20:09Z)
• CSformer: Bridging Convolution and Transformer for Compressive Sensing [65.22377493627687]
  This paper proposes a hybrid framework that integrates the advantages of leveraging detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing.
  arXiv  Detail & Related papers  (2021-12-31T04:37:11Z)
• Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation [3.974175960216864]
  We show that image complexity can be used as a guideline in choosing what is best for a given dataset. For high-complexity datasets, a shallow network running on the original images may yield better segmentation results than a deep network running on downsampled images.
  arXiv  Detail & Related papers  (2021-12-21T09:49:47Z)
• Fully Quantized Image Super-Resolution Networks [81.75002888152159]
  We propose a Fully Quantized image Super-Resolution framework (FQSR) to jointly optimize efficiency and accuracy. We apply our quantization scheme on multiple mainstream super-resolution architectures, including SRResNet, SRGAN and EDSR. Our FQSR using low bits quantization can achieve on par performance compared with the full-precision counterparts on five benchmark datasets.
  arXiv  Detail & Related papers  (2020-11-29T03:53:49Z)
• Efficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning Approach [0.0]
  We propose a multiobjective perspective on the training of neural networks by treating its prediction accuracy and the network complexity as two individual objective functions. Preliminary numerical results on exemplary convolutional neural networks confirm that large reductions in the complexity of neural networks with neglibile loss of accuracy are possible.
  arXiv  Detail & Related papers  (2020-08-31T13:28:03Z)
• When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks [57.0502745301132]
  We propose Micro-Dense Nets, a novel architecture with global residual learning and local micro-dense aggregations. Our micro-dense block can be integrated with neural architecture search based models to boost their performance.
  arXiv  Detail & Related papers  (2020-04-19T08:34:52Z)
• Widening and Squeezing: Towards Accurate and Efficient QNNs [125.172220129257]
  Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features.
  arXiv  Detail & Related papers  (2020-02-03T04:11:13Z)

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.