36 citations · 45 across the 8 of their papers we have counts for
11 papers
Embodied vision for learning object representations
Arthur Aubret, Céline Teulière, Jochen Triesch
Recent time-contrastive learning approaches manage to learn invariant object representations without supervision. This is achieved by mapping successive views of an object onto clo…
Recurrent Feedback Improves Recognition of Partially Occluded Objects
Markus Roland Ernst, Jochen Triesch, Thomas Burwick
Recurrent connectivity in the visual cortex is believed to aid object recognition for challenging conditions such as occlusion. Here we investigate if and how artificial neural net…
Learning Hierarchical Integration of Foveal and Peripheral Vision for Vergence Control by Active Efficient Coding
Zhetuo Zhao, Jochen Triesch, Bertram E. Shi
The active efficient coding (AEC) framework parsimoniously explains the joint development of visual processing and eye movements, e.g., the emergence of binocular disparity selecti…
Self-Calibrating Active Binocular Vision via Active Efficient Coding with Deep Autoencoders
Charles Wilmot, Bertram E. Shi, Jochen Triesch
We present a model of the self-calibration of active binocular vision comprising the simultaneous learning of visual representations, vergence, and pursuit eye movements. The model…
Learning Abstract Representations through Lossy Compression of Multi-Modal Signals
Charles Wilmot, Gianluca Baldassarre, Jochen Triesch
A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior. Abstract representations ignore specific det…
Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation
Diyuan Lu, Nenad Polomac, Iskra Gacheva +2
The application of Deep Learning (DL) for medical diagnosis is often hampered by two problems. First, the amount of training data may be scarce, as it is limited by the number of p…