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We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation.
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Anish Athalye, Logan Engstrom, Andrew Ilyas and Kevin Kwok · 2018
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“Sanity checks for saliency maps”
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“On the robustness of interpretability methods”
David Alvarez-Melis and Tommi Jaakkola · 2018
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Tom. Brown et al · 2018
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“Physical Adversarial Examples for Object Detectors”
Kevin Eykholt et al · 2018
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“SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications”
Abdullah Hamdi, Matthias Muller and Bernard Ghanem · 2018
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“Adversarial examples for generative models”
Jernej Kos, Ian Fischer and Dawn Song · 2018
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“Meta-sim: Learning to generate synthetic datasets”
Amlan Kar et al · 2019
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“Testing Robustness Against Unforeseen Adversaries”
Daniel Kang et al · 2019
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“Adversarial camera stickers: A physical camera-based attack on deep learning systems”
Juncheng Li, Frank. Schmidt and J. Kolter · 2019
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Hsueh-Ti Liu et al · 2019
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“Mitsuba 2: A Retargetable Forward and Inverse Renderer”
Merlin Nimier-David, Delio Vicini, Tizian Zeltner and Wenzel Jakob · 2019
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Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli and Pascal Frossard · 2018
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“Neural 3D Mesh Renderer”
Hiroharu Kato, Yoshitaka Ushiku and Tatsuya Harada · 2018
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“Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)”
Been Kim et al · 2018
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“Differentiable Monte Carlo Ray Tracing through Edge Sampling”
Tzu-Mao Li, Miika Aittala, Fredo Durand and Jaakko Lehtinen · 2018
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“The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.”
Zachary Lipton · 2018
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“Dpatch: An adversarial patch attack on object detectors”
Xin Liu et al · 2018
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“Virtualhome: Simulating household activities via programs”
Xavier Puig et al · 2018
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Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt and Vaishaal Shankar · 2019
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Vaishaal Shankar et al · 2019
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“Habitat: A platform for embodied ai research”
Manolis Savva et al · 2019
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“MeshAdv: Adversarial Meshes for Visual Recognition”
Chaowei Xiao et al · 2019
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“Debugging Tests for Model Explanations”
Julius Adebayo, Michael Muelly, Ilaria Liccardi and Been Kim · 2020
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Harkirat Behl et al · 2020
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“Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation”
Jeevan Devaranjan, Amlan Kar and Sanja Fidler · 2020
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“Identifying Statistical Bias in Dataset Replication”
Logan Engstrom et al · 2020
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“BlendTorch: A Real-Time, Adaptive Domain Randomization Library”
Christoph Heindl, Lukas Brunner, Sebastian Zambal and Josef Scharinger · 2020
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“Unity: A General Platform for Intelligent Agents”, 2020
Arthur Juliani et al · 2020
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Lakshya Jain et al · 2020
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“Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding”, arXiv 2020
Mike Roberts and Nathan Paczan · 2020
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“Identifying Model Weakness with Adversarial Examiner”
Michelle Shu, Chenxi Liu, Weichao Qiu and Alan Yuille · 2020
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“Flightmare: A Flexible Quadrotor Simulator”
Yunlong Song et al · 2020
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“Measuring Robustness to Natural Distribution Shifts in Image Classification”
Rohan Taori et al · 2020
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“Noise or signal: The role of image backgrounds in object recognition”
Kai Xiao, Logan Engstrom, Andrew Ilyas and Aleksander Madry · 2020
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“SAPIEN: A simulated part-based interactive environment”
Fanbo Xiang et al · 2020
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“Interactive Gibson Benchmark: A Benchmark for Interactive Navigation in Cluttered Environments”
Fei Xia et al · 2020
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“On Completeness-aware Concept-Based Explanations in Deep Neural Networks”
Chih-Kuan Yeh et al · 2020
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“robosuite: A modular simulation framework and benchmark for robot learning”
Yuke Zhu, Josiah Wong, Ajay Mandlekar and Roberto Martı́n-Martı́n · 2020
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“Leveraging Sparse Linear Layers for Debuggable Deep Networks”
Eric Wong, Shibani Santurkar and Aleksander Madry · 2021
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