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Found 4 papers, 1 papers with code
On Inherent Adversarial Robustness of Active Vision Systems
Current Deep Neural Networks are vulnerable to adversarial examples, which alter their predictions by adding carefully crafted noise.
Towards Two-Stream Foveation-based Active Vision Learning
Specifically, the proposed framework models the following mechanisms: 1) ventral (what) stream focusing on the input regions perceived by the fovea part of an eye (foveation), 2) dorsal (where) stream providing visual guidance, and 3) iterative processing of the two streams to calibrate visual focus and process the sequence of focused image patches.
Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift
We show that learning in this structured hierarchical manner results in networks that are more robust against subpopulation shifts, with an improvement up to 3\% in terms of accuracy and up to 11\% in terms of graphical distance over standard models on subpopulation shift benchmarks.
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
We propose a novel approach to generate such `semantic' adversarial examples by optimizing a particular adversarial loss over the range-space of a parametric conditional generative model.
