7 citations · 15 across the 5 of their papers we have counts for
8 papers
Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be
Valerio Biscione, Jeffrey S. Bowers
When seeing a new object, humans can immediately recognize it across different retinal locations: the internal object representation is invariant to translation. It is commonly bel…
Generalisation in Neural Networks Does not Require Feature Overlap
Jeff Mitchell, Jeffrey S. Bowers
That shared features between train and test data are required for generalisation in artificial neural networks has been a common assumption of both proponents and critics of these…
A case for robust translation tolerance in humans and CNNs. A commentary on Han et al
Ryan Blything, Valerio Biscione, Jeffrey Bowers
Han et al. (2020) reported a behavioral experiment that assessed the extent to which the human visual system can identify novel images at unseen retinal locations (what the authors…
Learning Translation Invariance in CNNs
Valerio Biscione, Jeffrey Bowers
When seeing a new object, humans can immediately recognize it across different retinal locations: we say that the internal object representation is invariant to translation. It is…
The human visual system and CNNs can both support robust online translation tolerance following extreme displacements
Ryan Blything, Valerio Biscione, Ivan I. Vankov +2
Visual translation tolerance refers to our capacity to recognize objects over a wide range of different retinal locations. Although translation is perhaps the simplest spatial tran…
Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?
Ella M. Gale, Nicholas Martin, Ryan Blything +2
Various methods of measuring unit selectivity have been developed with the aim of better understanding how neural networks work. But the different measures provide divergent estima…