Dimensionality of datasets in object detection networks

• URL: http://arxiv.org/abs/2210.07049v1
• Date: Thu, 13 Oct 2022 14:19:16 GMT
• Title: Dimensionality of datasets in object detection networks
• Authors: Ajay Chawda, Axel Vierling, Karsten Berns
• Abstract summary: convolutional neural networks (CNNs) are used in a large number of tasks in computer vision. One of them is object detection for autonomous driving. Our goal is to determine the effect of Intrinsic dimension (i.e. minimum number of parameters required to represent data) in different layers on the accuracy of object detection network for augmented data sets.
• Score: 0.5801044612920815
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In recent years, convolutional neural networks (CNNs) are used in a large number of tasks in computer vision. One of them is object detection for autonomous driving. Although CNNs are used widely in many areas, what happens inside the network is still unexplained on many levels. Our goal is to determine the effect of Intrinsic dimension (i.e. minimum number of parameters required to represent data) in different layers on the accuracy of object detection network for augmented data sets. Our investigation determines that there is difference between the representation of normal and augmented data during feature extraction.

Related papers

• Capsules as viewpoint learners for human pose estimation [4.246061945756033]
  We show how most neural networks are not able to generalize well when the camera is subject to significant viewpoint changes. We propose a novel end-to-end viewpoint-equivariant capsule autoencoder that employs a fast Variational Bayes routing and matrix capsules. We achieve state-of-the-art results for multiple tasks and datasets while retaining other desirable properties.
  arXiv  Detail & Related papers  (2023-02-13T09:01:46Z)
• What Can Be Learnt With Wide Convolutional Neural Networks? [69.55323565255631]
  We study infinitely-wide deep CNNs in the kernel regime. We prove that deep CNNs adapt to the spatial scale of the target function. We conclude by computing the generalisation error of a deep CNN trained on the output of another deep CNN.
  arXiv  Detail & Related papers  (2022-08-01T17:19:32Z)
• Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches [3.255610188565679]
  CNNs allow to work on large-scale size of data, as well as cover different scenarios for a specific task. Many networks have been proposed and become the famous networks used for any DL models in any AI task. A backbone is a known network trained in many other tasks before and demonstrates its effectiveness.
  arXiv  Detail & Related papers  (2022-06-16T09:18:34Z)
• Network Comparison Study of Deep Activation Feature Discriminability with Novel Objects [0.5076419064097732]
  State-of-the-art computer visions algorithms have incorporated Deep Neural Networks (DNN) in feature extracting roles, creating Deep Convolutional Activation Features (DeCAF) This study analyzes the general discriminability of novel object visual appearances encoded into the DeCAF space of six of the leading visual recognition DNN architectures.
  arXiv  Detail & Related papers  (2022-02-08T07:40:53Z)
• Dive into Layers: Neural Network Capacity Bounding using Algebraic Geometry [55.57953219617467]
  We show that the learnability of a neural network is directly related to its size. We use Betti numbers to measure the topological geometric complexity of input data and the neural network. We perform the experiments on a real-world dataset MNIST and the results verify our analysis and conclusion.
  arXiv  Detail & Related papers  (2021-09-03T11:45:51Z)
• Dataset for eye-tracking tasks [0.0]
  We present a dataset that is suitable for training custom models of convolutional neural networks for eye-tracking tasks. This dataset contains 10,000 eye images in an extension of 416 by 416 pixels. This manuscript can be considered as a guide for the preparation of datasets for eye-tracking devices.
  arXiv  Detail & Related papers  (2021-06-01T23:54:23Z)
• 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)
• Understanding the Role of Individual Units in a Deep Neural Network [85.23117441162772]
  We present an analytic framework to systematically identify hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes.
  arXiv  Detail & Related papers  (2020-09-10T17:59:10Z)
• Ventral-Dorsal Neural Networks: Object Detection via Selective Attention [51.79577908317031]
  We propose a new framework called Ventral-Dorsal Networks (VDNets) Inspired by the structure of the human visual system, we propose the integration of a "Ventral Network" and a "Dorsal Network" Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-15T23:57:36Z)
• When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene Recognition [10.796613905980609]
  We propose a novel framework that extracts discriminative feature representations from multi-modal RGB-D images for object and scene recognition tasks. To cope with the high dimensionality of CNN activations, a random weighted pooling scheme has been proposed. Experiments verify that fully randomized structure in RNN stage encodes CNN activations to discriminative solid features successfully.
  arXiv  Detail & Related papers  (2020-04-26T10:58:27Z)
• Analyzing Neural Networks Based on Random Graphs [77.34726150561087]
  We perform a massive evaluation of neural networks with architectures corresponding to random graphs of various types. We find that none of the classical numerical graph invariants by itself allows to single out the best networks. We also find that networks with primarily short-range connections perform better than networks which allow for many long-range connections.
  arXiv  Detail & Related papers  (2020-02-19T11:04:49Z)

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.