Impact of Scaled Image on Robustness of Deep Neural Networks

• URL: http://arxiv.org/abs/2209.02132v2
• Date: Tue, 23 May 2023 15:46:32 GMT
• Title: Impact of Scaled Image on Robustness of Deep Neural Networks
• Authors: Chengyin Hu, Weiwen Shi
• Abstract summary: Scaling the raw images creates out-of-distribution data, which makes it a possible adversarial attack to fool the networks. In this work, we propose a Scaling-distortion dataset ImageNet-CS by Scaling a subset of the ImageNet Challenge dataset by different multiples.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNNs) have been widely used in computer vision tasks like image classification, object detection and segmentation. Whereas recent studies have shown their vulnerability to manual digital perturbations or distortion in the input images. The accuracy of the networks is remarkably influenced by the data distribution of their training dataset. Scaling the raw images creates out-of-distribution data, which makes it a possible adversarial attack to fool the networks. In this work, we propose a Scaling-distortion dataset ImageNet-CS by Scaling a subset of the ImageNet Challenge dataset by different multiples. The aim of our work is to study the impact of scaled images on the performance of advanced DNNs. We perform experiments on several state-of-the-art deep neural network architectures on the proposed ImageNet-CS, and the results show a significant positive correlation between scaling size and accuracy decline. Moreover, based on ResNet50 architecture, we demonstrate some tests on the performance of recent proposed robust training techniques and strategies like Augmix, Revisiting and Normalizer Free on our proposed ImageNet-CS. Experiment results have shown that these robust training techniques can improve networks' robustness to scaling transformation.

Related papers

• 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)
• Deformable Convolution Based Road Scene Semantic Segmentation of Fisheye Images in Autonomous Driving [4.720434481945155]
  This study investigates the effectiveness of modern Deformable Convolutional Neural Networks (DCNNs) for semantic segmentation tasks. Our experiments focus on segmenting the WoodScape fisheye image dataset into ten distinct classes, assessing the Deformable Networks' ability to capture intricate spatial relationships. The significant improvement in mIoU score resulting from integrating Deformable CNNs demonstrates their effectiveness in handling the geometric distortions present in fisheye imagery.
  arXiv  Detail & Related papers  (2024-07-23T17:02:24Z)
• Image edge enhancement for effective image classification [7.470763273994321]
  We propose an edge enhancement-based method to enhance both accuracy and training speed of neural networks. Our approach involves extracting high frequency features, such as edges, from images within the available dataset and fusing them with the original images.
  arXiv  Detail & Related papers  (2024-01-13T10:01:34Z)
• Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks [5.089732183029123]
  In digital rock physics, analysing microstructures from CT and SEM scans is crucial for estimating properties like porosity and pore connectivity. Traditional segmentation methods like thresholding and CNNs often fall short in accurately detailing rock microstructures and are prone to noise. U-Net improved segmentation accuracy but required many expert-annotated samples, a laborious and error-prone process due to complex pore shapes. Our study employed an advanced generative AI model, the diffusion model, to overcome these limitations. TransU-Net sets a new standard in digital rock physics, paving the way for future geoscience and engineering breakthroughs.
  arXiv  Detail & Related papers  (2023-11-10T14:24:50Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Impact of Colour Variation on Robustness of Deep Neural Networks [0.0]
  Deep neural networks (DNNs) have shown state-of-the-art performance for computer vision applications like image classification, segmentation and object detection. Recent advances have shown their vulnerability to manual digital perturbations in the input data, namely adversarial attacks. In this work, we propose a color-variation dataset by distorting their RGB color on a subset of the ImageNet with 27 different combinations.
  arXiv  Detail & Related papers  (2022-09-02T08:16:04Z)
• FuNNscope: Visual microscope for interactively exploring the loss landscape of fully connected neural networks [77.34726150561087]
  We show how to explore high-dimensional landscape characteristics of neural networks. We generalize observations on small neural networks to more complex systems. An interactive dashboard opens up a number of possible application networks.
  arXiv  Detail & Related papers  (2022-04-09T16:41:53Z)
• Segmentation of Roads in Satellite Images using specially modified U-Net CNNs [0.0]
  The aim of this paper is to build an image classifier for satellite images of urban scenes that identifies the portions of the images in which a road is located. Unlike conventional computer vision algorithms, convolutional neural networks (CNNs) provide accurate and reliable results on this task.
  arXiv  Detail & Related papers  (2021-09-29T19:08:32Z)
• SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size [95.00667357120442]
  Changing the object location, rotation and size may affect the predictions in non-trivial ways. We perform a fine-grained analysis of robustness with respect to these factors of variation using SI-Score, a synthetic dataset.
  arXiv  Detail & Related papers  (2021-04-09T05:00:49Z)
• On Robustness and Transferability of Convolutional Neural Networks [147.71743081671508]
  Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. We study the interplay between out-of-distribution and transfer performance of modern image classification CNNs for the first time. We find that increasing both the training set and model sizes significantly improve the distributional shift robustness.
  arXiv  Detail & Related papers  (2020-07-16T18:39:04Z)
• Learning to Learn Parameterized Classification Networks for Scalable Input Images [76.44375136492827]
  Convolutional Neural Networks (CNNs) do not have a predictable recognition behavior with respect to the input resolution change. We employ meta learners to generate convolutional weights of main networks for various input scales. We further utilize knowledge distillation on the fly over model predictions based on different input resolutions.
  arXiv  Detail & Related papers  (2020-07-13T04:27:25Z)

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.