Decision boundaries and convex hulls in the feature space that deep learning functions learn from images

• URL: http://arxiv.org/abs/2202.04052v1
• Date: Sat, 5 Feb 2022 15:09:51 GMT
• Title: Decision boundaries and convex hulls in the feature space that deep learning functions learn from images
• Authors: Roozbeh Yousefzadeh
• Abstract summary: We study the properties of a low-dimensional manifold that models extract and learn from images. For image classification models, their last hidden layer is the one where images of each class is separated from other classes and it also has the least number of features. We observe that geometric arrangements of decision boundaries in feature space is significantly different compared to pixel space.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of deep neural networks in image classification and learning can be partly attributed to the features they extract from images. It is often speculated about the properties of a low-dimensional manifold that models extract and learn from images. However, there is not sufficient understanding about this low-dimensional space based on theory or empirical evidence. For image classification models, their last hidden layer is the one where images of each class is separated from other classes and it also has the least number of features. Here, we develop methods and formulations to study that feature space for any model. We study the partitioning of the domain in feature space, identify regions guaranteed to have certain classifications, and investigate its implications for the pixel space. We observe that geometric arrangements of decision boundaries in feature space is significantly different compared to pixel space, providing insights about adversarial vulnerabilities, image morphing, extrapolation, ambiguity in classification, and the mathematical understanding of image classification models.

Related papers

• EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models [52.3015009878545]
  We develop an image segmentor capable of generating fine-grained segmentation maps without any additional training. Our framework identifies semantic correspondences between image pixels and spatial locations of low-dimensional feature maps. In extensive experiments, the produced segmentation maps are demonstrated to be well delineated and capture detailed parts of the images.
  arXiv  Detail & Related papers  (2024-01-22T07:34:06Z)
• Why do CNNs excel at feature extraction? A mathematical explanation [53.807657273043446]
  We introduce a novel model for image classification, based on feature extraction, that can be used to generate images resembling real-world datasets. In our proof, we construct piecewise linear functions that detect the presence of features, and show that they can be realized by a convolutional network.
  arXiv  Detail & Related papers  (2023-07-03T10:41:34Z)
• Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers [8.781861951759948]
  This paper presents Geo-SIC, the first deep learning model to learn deformable shapes in a deformation space for an improved performance of image classification. We introduce a newly designed framework that (i) simultaneously derives features from both image and latent shape spaces with large intra-class variations. We develop a boosted classification network, equipped with an unsupervised learning of geometric shape representations.
  arXiv  Detail & Related papers  (2022-10-25T01:55:17Z)
• A domain adaptive deep learning solution for scanpath prediction of paintings [66.46953851227454]
  This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. We introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention.
  arXiv  Detail & Related papers  (2022-09-22T22:27:08Z)
• Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images [65.74185962364211]
  We present a method for incorporating neighborhood information into distance-based dimensionality reduction methods. Based on a classification of different methods for comparing image patches, we explore a number of different approaches.
  arXiv  Detail & Related papers  (2022-02-18T13:17:43Z)
• Extrapolation Frameworks in Cognitive Psychology Suitable for Study of Image Classification Models [0.0]
  In contrast to the deep learning literature, in cognitive science, psychology, and neuroscience, extrapolation and learning are often studied in tandem. We propose a novel extrapolation framework for the mathematical study of deep learning models.
  arXiv  Detail & Related papers  (2021-12-06T23:06:31Z)
• Topological Semantic Mapping by Consolidation of Deep Visual Features [0.0]
  This work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN, the GoogLeNet, from 2D images captured in multiple views of the environment as the robot operates. The experiments, performed using a real-world indoor dataset, showed that the method is able to consolidate the visual features of regions and use them to recognize objects and place categories as semantic properties.
  arXiv  Detail & Related papers  (2021-06-24T01:10:03Z)
• Low-Rank Subspaces in GANs [101.48350547067628]
  This work introduces low-rank subspaces that enable more precise control of GAN generation. LowRankGAN is able to find the low-dimensional representation of attribute manifold. Experiments on state-of-the-art GAN models (including StyleGAN2 and BigGAN) trained on various datasets demonstrate the effectiveness of our LowRankGAN.
  arXiv  Detail & Related papers  (2021-06-08T16:16:32Z)
• Attention Model Enhanced Network for Classification of Breast Cancer Image [54.83246945407568]
  AMEN is formulated in a multi-branch fashion with pixel-wised attention model and classification submodular. To focus more on subtle detail information, the sample image is enhanced by the pixel-wised attention map generated from former branch. Experiments conducted on three benchmark datasets demonstrate the superiority of the proposed method under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:44:21Z)
• Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images [34.95218692917125]
  We present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters.
  arXiv  Detail & Related papers  (2020-09-03T00:38:45Z)
• Multiple View Generation and Classification of Mid-wave Infrared Images using Deep Learning [1.52292571922932]
  We propose a novel study of generating unseen arbitrary viewpoints for infrared imagery in the non-linear feature subspace. Our approach on the contrary understands the semantic information in natural images and encapsulates it such that our predicted unseen views possess good 3D representations.
  arXiv  Detail & Related papers  (2020-08-18T02:58:21Z)

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.