51,735 research outputs found
When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks
Discovering and exploiting the causality in deep neural networks (DNNs) are
crucial challenges for understanding and reasoning causal effects (CE) on an
explainable visual model. "Intervention" has been widely used for recognizing a
causal relation ontologically. In this paper, we propose a causal inference
framework for visual reasoning via do-calculus. To study the intervention
effects on pixel-level features for causal reasoning, we introduce pixel-wise
masking and adversarial perturbation. In our framework, CE is calculated using
features in a latent space and perturbed prediction from a DNN-based model. We
further provide the first look into the characteristics of discovered CE of
adversarially perturbed images generated by gradient-based methods
\footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}.
Experimental results show that CE is a competitive and robust index for
understanding DNNs when compared with conventional methods such as
class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for
human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds
promises for detecting adversarial examples as it possesses distinct
characteristics in the presence of adversarial perturbations.Comment: Noted our camera-ready version has changed the title. "When Causal
Intervention Meets Adversarial Examples and Image Masking for Deep Neural
Networks" as the v3 official paper title in IEEE Proceeding. Please use it in
your formal reference. Accepted at IEEE ICIP 2019. Pytorch code has released
on https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvIm
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects
Understanding the behavior of stochastic gradient descent (SGD) in the
context of deep neural networks has raised lots of concerns recently. Along
this line, we study a general form of gradient based optimization dynamics with
unbiased noise, which unifies SGD and standard Langevin dynamics. Through
investigating this general optimization dynamics, we analyze the behavior of
SGD on escaping from minima and its regularization effects. A novel indicator
is derived to characterize the efficiency of escaping from minima through
measuring the alignment of noise covariance and the curvature of loss function.
Based on this indicator, two conditions are established to show which type of
noise structure is superior to isotropic noise in term of escaping efficiency.
We further show that the anisotropic noise in SGD satisfies the two conditions,
and thus helps to escape from sharp and poor minima effectively, towards more
stable and flat minima that typically generalize well. We systematically design
various experiments to verify the benefits of the anisotropic noise, compared
with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics).Comment: ICML 2019 camera read
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