5 citations · 7 across the 4 of their papers we have counts for
4 papers
Modularity Trumps Invariance for Compositional Robustness
Ian Mason, Anirban Sarkar, Tomotake Sasaki +1
By default neural networks are not robust to changes in data distribution. This has been demonstrated with simple image corruptions, such as blurring or adding noise, degrading ima…
Do Neural Networks for Segmentation Understand Insideness?
Kimberly Villalobos, Vilim Štih, Amineh Ahmadinejad +6
The insideness problem is an aspect of image segmentation that consists of determining which pixels are inside and outside a region. Deep Neural Networks (DNNs) excel in segmentati…
Symmetry Perception by Deep Networks: Inadequacy of Feed-Forward Architectures and Improvements with Recurrent Connections
Shobhita Sundaram, Darius Sinha, Matthew Groth +2
Symmetry is omnipresent in nature and perceived by the visual system of many species, as it facilitates detecting ecologically important classes of objects in our environment. Symm…
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions
Vanessa D'Amario, Sanjana Srivastava, Tomotake Sasaki +1
Datasets often contain input dimensions that are unnecessary to predict the output label, e.g. background in object recognition, which lead to more trainable parameters. Deep Neura…