How saccadic vision might help with theinterpretability of deep networks
arXiv:2105.13264v1
Originality Incremental advance
AI Analysis
This addresses interpretability issues in deep learning for researchers and practitioners, but it appears incremental as it adapts an existing biological mechanism.
The paper tackles the interpretability and lack of object-orientedness in deep networks by proposing a biologically plausible saccadic vision model, with proof-of-concept experimental results provided to support the approach.
We describe how some problems (interpretability,lack of object-orientedness) of modern deep networks potentiallycould be solved by adapting a biologically plausible saccadicmechanism of perception. A sketch of such a saccadic visionmodel is proposed. Proof of concept experimental results areprovided to support the proposed approach.