Shape or Texture: Understanding Discriminative Features in CNNs

• URL: http://arxiv.org/abs/2101.11604v1
• Date: Wed, 27 Jan 2021 18:54:00 GMT
• Title: Shape or Texture: Understanding Discriminative Features in CNNs
• Authors: Md Amirul Islam, Matthew Kowal, Patrick Esser, Sen Jia, Bjorn Ommer, Konstantinos G. Derpanis, Neil Bruce
• Abstract summary: Recent studies have shown that CNNs actually exhibit a texture bias' We show that a network learns the majority of overall shape information at the first few epochs of training. We also show that the encoding of shape does not imply the encoding of localized per-pixel semantic information.
• Score: 28.513300496205044
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a `texture bias': given an image with both texture and shape cues (e.g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture. However, these previous studies conduct experiments on the final classification output of the network, and fail to robustly evaluate the bias contained (i) in the latent representations, and (ii) on a per-pixel level. In this paper, we design a series of experiments that overcome these issues. We do this with the goal of better understanding what type of shape information contained in the network is discriminative, where shape information is encoded, as well as when the network learns about object shape during training. We show that a network learns the majority of overall shape information at the first few epochs of training and that this information is largely encoded in the last few layers of a CNN. Finally, we show that the encoding of shape does not imply the encoding of localized per-pixel semantic information. The experimental results and findings provide a more accurate understanding of the behaviour of current CNNs, thus helping to inform future design choices.

Related papers

• Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation [16.343080265661882]
  Shape learning could be a desirable property of convolutional neural networks (CNNs) when target objects have specific shapes. We present a new behavioral metric to measure the extent to which a CNN utilizes shape information. We conclude that CNNs do not learn shape in typical settings but rather rely on other features available to identify the objects of interest.
  arXiv  Detail & Related papers  (2023-05-11T05:02:11Z)
• Random Padding Data Augmentation [23.70951896315126]
  convolutional neural network (CNN) learns the same object in different positions in images. The usefulness of the features' spatial information in CNNs has not been well investigated. We introduce Random Padding, a new type of padding method for training CNNs.
  arXiv  Detail & Related papers  (2023-02-17T04:15:33Z)
• Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks [61.60177890353585]
  Deep convolutional neural networks (CNNs) have been shown to provide excellent models for its functional analogue in the brain, the ventral stream in visual cortex. Here we consider some prominent statistical patterns that are known to exist in the internal representations of either CNNs or the visual cortex. We show that CNNs and visual cortex share a similarly tight relationship between dimensionality expansion/reduction of object representations and reformatting of image information.
  arXiv  Detail & Related papers  (2022-05-27T08:06:40Z)
• Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial [7.826806223782053]
  Convolutional Neural Networks (CNN) are a de-facto standard for the analysis of large volumes of signals and images. We revisit their operation from first principles and a matched filtering perspective. It is our hope that this tutorial will help shed new light and physical intuition into the understanding and further development of deep neural networks.
  arXiv  Detail & Related papers  (2021-08-26T09:07:49Z)
• The Mind's Eye: Visualizing Class-Agnostic Features of CNNs [92.39082696657874]
  We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer. Our method uses a dual-objective activation and distance loss, without requiring a generator network nor modifications to the original model.
  arXiv  Detail & Related papers  (2021-01-29T07:46:39Z)
• Assessing The Importance Of Colours For CNNs In Object Recognition [70.70151719764021]
  Convolutional neural networks (CNNs) have been shown to exhibit conflicting properties. We demonstrate that CNNs often rely heavily on colour information while making a prediction. We evaluate a model trained with congruent images on congruent, greyscale, and incongruent images.
  arXiv  Detail & Related papers  (2020-12-12T22:55:06Z)
• Informative Dropout for Robust Representation Learning: A Shape-bias Perspective [84.30946377024297]
  We propose a light-weight model-agnostic method, namely Informative Dropout (InfoDrop), to improve interpretability and reduce texture bias. Specifically, we discriminate texture from shape based on local self-information in an image, and adopt a Dropout-like algorithm to decorrelate the model output from the local texture.
  arXiv  Detail & Related papers  (2020-08-10T16:52:24Z)
• Teaching CNNs to mimic Human Visual Cognitive Process & regularise Texture-Shape bias [18.003188982585737]
  Recent experiments in computer vision demonstrate texture bias as the primary reason for supreme results in models employing Convolutional Neural Networks (CNNs) It is believed that the cost function forces the CNN to take a greedy approach and develop a proclivity for local information like texture to increase accuracy, thus failing to explore any global statistics. We propose CognitiveCNN, a new intuitive architecture, inspired from feature integration theory in psychology to utilise human interpretable feature like shape, texture, edges etc. to reconstruct, and classify the image.
  arXiv  Detail & Related papers  (2020-06-25T22:32:54Z)
• An Information-theoretic Visual Analysis Framework for Convolutional Neural Networks [11.15523311079383]
  We introduce a data model to organize the data that can be extracted from CNN models. We then propose two ways to calculate entropy under different circumstances. We develop a visual analysis system, CNNSlicer, to interactively explore the amount of information changes inside the model.
  arXiv  Detail & Related papers  (2020-05-02T21:36:50Z)
• Shape-Oriented Convolution Neural Network for Point Cloud Analysis [59.405388577930616]
  Point cloud is a principal data structure adopted for 3D geometric information encoding. Shape-oriented message passing scheme dubbed ShapeConv is proposed to focus on the representation learning of the underlying shape formed by each local neighboring point.
  arXiv  Detail & Related papers  (2020-04-20T16:11:51Z)
• Curriculum By Smoothing [52.08553521577014]
  Convolutional Neural Networks (CNNs) have shown impressive performance in computer vision tasks such as image classification, detection, and segmentation. We propose an elegant curriculum based scheme that smoothes the feature embedding of a CNN using anti-aliasing or low-pass filters. As the amount of information in the feature maps increases during training, the network is able to progressively learn better representations of the data.
  arXiv  Detail & Related papers  (2020-03-03T07:27:44Z)

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.