Been Kim
Heatmapping.org
Deep neural networks (DNNs) have no doubt brought great successes to a wide range of applications in computer vision, computational linguistics and AI. However, foundational principles underlying the DNNs’ success and their resilience to adversarial attacks are still largely missing. Interpreting and theorizing the internal mechanisms of DNNs becomes a compelling yet controversial topic. The statistical methods and rule-based methods for network interpretation have much to offer in semantically disentanglin
This workshop aims to bring together researchers, engineers as well as industrial practitioners, who concern about interpretability, safety, and reliability of artificial intelligence. Joint force efforts along this direction are expected to open the black box of DNNs and, ultimately, to bridge the gap between connectionism and symbolism of AI research. The main theme of the workshop is therefore to build up consensus on a variety of topics including motivations, typical methodologies, prospective innovatio
This tutorial is to broadly engage the computer vision community with the topic of interpretability and explainability in models used in computer vision. We will introduce the definition of interpretability and why it is important, and have a review on visualization and interpretation methodologies for analyzing both the data and the models in computer vision.
Been Kim
Heatmapping.org