51 research outputs found
REVAMP: Automated Simulations of Adversarial Attacks on Arbitrary Objects in Realistic Scenes
Deep Learning models, such as those used in an autonomous vehicle are
vulnerable to adversarial attacks where an attacker could place an adversarial
object in the environment, leading to mis-classification. Generating these
adversarial objects in the digital space has been extensively studied, however
successfully transferring these attacks from the digital realm to the physical
realm has proven challenging when controlling for real-world environmental
factors. In response to these limitations, we introduce REVAMP, an easy-to-use
Python library that is the first-of-its-kind tool for creating attack scenarios
with arbitrary objects and simulating realistic environmental factors,
lighting, reflection, and refraction. REVAMP enables researchers and
practitioners to swiftly explore various scenarios within the digital realm by
offering a wide range of configurable options for designing experiments and
using differentiable rendering to reproduce physically plausible adversarial
objects. We will demonstrate and invite the audience to try REVAMP to produce
an adversarial texture on a chosen object while having control over various
scene parameters. The audience will choose a scene, an object to attack, the
desired attack class, and the number of camera positions to use. Then, in real
time, we show how this altered texture causes the chosen object to be
mis-classified, showcasing the potential of REVAMP in real-world scenarios.
REVAMP is open-source and available at https://github.com/poloclub/revamp
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization
Given thousands of equally accurate machine learning (ML) models, how can
users choose among them? A recent ML technique enables domain experts and data
scientists to generate a complete Rashomon set for sparse decision trees--a
huge set of almost-optimal interpretable ML models. To help ML practitioners
identify models with desirable properties from this Rashomon set, we develop
TimberTrek, the first interactive visualization system that summarizes
thousands of sparse decision trees at scale. Two usage scenarios highlight how
TimberTrek can empower users to easily explore, compare, and curate models that
align with their domain knowledge and values. Our open-source tool runs
directly in users' computational notebooks and web browsers, lowering the
barrier to creating more responsible ML models. TimberTrek is available at the
following public demo link: https://poloclub.github.io/timbertrek.Comment: Accepted at IEEE VIS 2022. 5 pages, 6 figures. For a demo video, see
https://youtu.be/3eGqTmsStJM. For a live demo, visit
https://poloclub.github.io/timbertre
CNN 101: Interactive Visual Learning for Convolutional Neural Networks
The success of deep learning solving previously-thought hard problems has
inspired many non-experts to learn and understand this exciting technology.
However, it is often challenging for learners to take the first steps due to
the complexity of deep learning models. We present our ongoing work, CNN 101,
an interactive visualization system for explaining and teaching convolutional
neural networks. Through tightly integrated interactive views, CNN 101 offers
both overview and detailed descriptions of how a model works. Built using
modern web technologies, CNN 101 runs locally in users' web browsers without
requiring specialized hardware, broadening the public's education access to
modern deep learning techniques.Comment: CHI'20 Late-Breaking Work (April 25-30, 2020), 7 pages, 3 figure
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