Dimensionality of datasets in object detection networks
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 13 Oct 2022.
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12 of 18 references.
References · 12
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2016Understanding deep learning requires rethinking generalizationcited by 1,657
- 2018Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionalitycited by 280
- 2019Intrinsic dimension of data representations in deep neural networkscited by 276
- 2018Estimating the intrinsic dimension of datasets by a minimal neighborhood informationcited by 246
- 2018Dimensionality-Driven Learning with Noisy Labelscited by 173
- 2018On the Intrinsic Dimensionality of Image Representationscited by 57
- 2022Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysiscited by 22
- 2020Local intrinsic dimensionality estimators based on concentration of measurecited by 5
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