24 citations · 78 across the 6 of their papers we have counts for
4 papers · 2 filters
Inability of spatial transformations of CNN feature maps to support invariant recognition
Ylva Jansson, Maksim Maydanskiy, Lukas Finnveden +1
A large number of deep learning architectures use spatial transformations of CNN feature maps or filters to better deal with variability in object appearance caused by natural imag…
Understanding when spatial transformer networks do not support invariance, and what to do about it
Lukas Finnveden, Ylva Jansson, Tony Lindeberg
Spatial transformer networks (STNs) were designed to enable convolutional neural networks (CNNs) to learn invariance to image transformations. STNs were originally proposed to tran…
Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges
Ylva Jansson, Tony Lindeberg
The ability to handle large scale variations is crucial for many real world visual tasks. A straightforward approach for handling scale in a deep network is to process an image at…
The problems with using STNs to align CNN feature maps
Lukas Finnveden, Ylva Jansson, Tony Lindeberg
Spatial transformer networks (STNs) were designed to enable CNNs to learn invariance to image transformations. STNs were originally proposed to transform CNN feature maps as well a…