Search Results for author:
Found 6 papers, 2 papers with code
Rethinking interpretation: Input-agnostic saliency mapping of deep visual classifiers
Addressing the gap, we introduce a new perspective of input-agnostic saliency mapping that computationally estimates the high-level features attributed by the model to its outputs.
CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency
Backpropagation image saliency aims at explaining model predictions by estimating model-centric importance of individual pixels in the input.
Orthogonal Deep Models As Defense Against Black-Box Attacks
On the other, deep learning has also been found vulnerable to adversarial attacks, which calls for new techniques to defend deep models against these attacks.
Attack to Explain Deep Representation
The accumulated signal gradually manifests itself as a collection of visually salient features of the target label (in model fooling), casting adversarial perturbations as primitive features of the target label.
Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Cloud
We use this concept to localize the position and orientation (pose) of the camera of a query image in dense point clouds.
Label Universal Targeted Attack
We introduce Label Universal Targeted Attack (LUTA) that makes a deep model predict a label of attacker's choice for `any' sample of a given source class with high probability.
