• Corpus ID: 235422265

On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification

@inproceedings{Babaiee2021OnOffCR,
  title={On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification},
  author={Zahra Babaiee and Ramin M. Hasani and Mathias Lechner and Daniela Rus and Radu Grosu},
  booktitle={ICML},
  year={2021}
}
Robustness to variations in lighting conditions is a key objective for any deep vision system. To this end, our paper extends the receptive field of convolutional neural networks with two residual components, ubiquitous in the visual processing system of vertebrates: On-center and off-center pathways, with excitatory center and inhibitory surround; OOCS for short. The on-center pathway is excited by the presence of a light stimulus in its center, but not in its surround, whereas the off-center… 
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4 Citations
Background Citations
2
Methods Citations
1

4 Citations

A precortical module for robust CNNs to light variations
TLDR
The analogies between the cortical level of the visual system and the structure of popular CNNs suggests the introduction of an additional preliminary convolutional module inspired to precortical neuronal circuits to improve robustness with respect to global light intensity and contrast variations in the input images.
3D-OOCS: Learning Prostate Segmentation with Inductive Bias
TLDR
This work intro-duce OOCS-enhanced networks, a novel architecture inspired by the innate nature of visual processing in the vertebrates that helps 3D U-Nets to scrutinise and delineate anatomical structures present in 3D images with increased accuracy.
Interactive Analysis of CNN Robustness
TLDR
Perturber is a web‐based application that allows users to instantaneously explore how CNN activations and predictions evolve when a 3D input scene is interactively perturbed, and replicate users’ insights with other CNN architectures and input images, yielding new insights about the vulnerability of adversarially trained models.
GoTube: Scalable Stochastic Verification of Continuous-Depth Models
TLDR
This work introduces a new stochastic verification algorithm that formally quantifies the behavioral robustness of any timecontinuous process formulated as a continuous-depth model and calls it GoTube, which is stable and sets the state of the art in terms of its ability to scale to time horizons well beyond what has been previously possible.

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