Personne
Adrien Christophe Doerig
Cette personne n’est plus à l’EPFL
Publications associées (34)
Why computational complexity may set impenetrable barriers for epistemic reductionism
Michael Herzog, Christian Sachse, Adrien Christophe Doerig
According to physicalism, everything is physical or metaphysically connected to the physical. If physicalism were true, it seems that we should - in principle - be able to reduce the descriptions and explanations of special sciences to physical ones, for e ...
Dordrecht2023Fixing the problems of deep neural networks will require better training data and learning algorithms
Martin Schrimpf, Adrien Christophe Doerig, Matthias Bethge, Jianghao Liu, Kuntal Ghosh
Bowers et al. argue that deep neural networks (DNNs) are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We show that this problem is worsening as DNNs ar ...
Cambridge2023First-person experience cannot rescue causal structure theories from the unfolding argument
Michael Herzog, Adrien Christophe Doerig, Aaron Schurger
We recently put forward an argument, the Unfolding Argument (UA), that integrated information theory (IIT) and other causal structure theories are either already falsified or unfalsifiable, which provoked significant criticism. It seems that we and the cri ...
Elsevier2022Global information processing in feedforward deep networks
Michael Herzog, Ben Henrik Lönnqvist, Adrien Christophe Doerig, Alban Bornet
While deep neural networks are state-of-the-art models of many parts of the human visual system, here we show that they fail to process global information in a humanlike manner. First, using visual crowding as a probe into global visual information process ...
2022How crowding challenges (feedforward) convolutional neural networks
Michael Herzog, Ben Henrik Lönnqvist, Gregory Francis, Adrien Christophe Doerig, Alban Bornet, Lynn Schmittwilken
Are (feedforward) convolutional neural networks (CNNs) good models for the human visual system? Here, we used visual crowding as a well-controlled psychophysical test to probe CNNs. Visual crowding is a ubiquitous breakdown of object recognition in the hum ...
2021A new empirical challenge for local theories of consciousness
Local theories of consciousness state that one is conscious of a feature if it is adequately represented and processed in sensory brain areas, given some background conditions. We challenge the core prediction of local theories based on long-lasting postdi ...
WILEY2021Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing
Michael Herzog, Adrien Christophe Doerig, Mauro Manassi, Alban Bornet, Oh-Hyeon Choung
In visual crowding, the perception of a target deteriorates in the presence of nearby flankers. Traditionally, target-flanker interactions have been considered as local, mostly deleterious, low-level, and feature specific, occurring when information is poo ...
ASSOC RESEARCH VISION OPHTHALMOLOGY INC2021Shrinking Bouma's window: How to model crowding in dense displays
Michael Herzog, Gregory Francis, Adrien Christophe Doerig, Alban Bornet
In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma's law, i.e., all elements within a certain distance interfere with the target, and that adding more elements ...
PUBLIC LIBRARY SCIENCE2021Capsule networks as recurrent models of grouping and segmentation
Michael Herzog, Adrien Christophe Doerig, Bilge Sayim, Mauro Manassi, Lynn Schmittwilken
Classically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we pre ...
2020Crowding and the Architecture of the Visual System
Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...
EPFL2020