Search Results for author:
Found 4 papers, 1 papers with code
ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]
We propose ProtoArgNet, a novel interpretable deep neural architecture for image classification in the spirit of prototypical-part-learning as found, e. g. in ProtoPNet.
CAFE: Conflict-Aware Feature-wise Explanations
Feature attribution methods are widely used to explain neural models by determining the influence of individual input features on the models' outputs.
SpArX: Sparse Argumentative Explanations for Neural Networks [Technical Report]
Neural networks (NNs) have various applications in AI, but explaining their decisions remains challenging.
GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping
The obtained dataset is then used to train the GraspCaps network.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
