68,216 research outputs found
Interpreting Adversarially Trained Convolutional Neural Networks
We attempt to interpret how adversarially trained convolutional neural
networks (AT-CNNs) recognize objects. We design systematic approaches to
interpret AT-CNNs in both qualitative and quantitative ways and compare them
with normally trained models. Surprisingly, we find that adversarial training
alleviates the texture bias of standard CNNs when trained on object recognition
tasks, and helps CNNs learn a more shape-biased representation. We validate our
hypothesis from two aspects. First, we compare the salience maps of AT-CNNs and
standard CNNs on clean images and images under different transformations. The
comparison could visually show that the prediction of the two types of CNNs is
sensitive to dramatically different types of features. Second, to achieve
quantitative verification, we construct additional test datasets that destroy
either textures or shapes, such as style-transferred version of clean data,
saturated images and patch-shuffled ones, and then evaluate the classification
accuracy of AT-CNNs and normal CNNs on these datasets. Our findings shed some
light on why AT-CNNs are more robust than those normally trained ones and
contribute to a better understanding of adversarial training over CNNs from an
interpretation perspective.Comment: To apper in ICML1
Sparsity Invariant CNNs
In this paper, we consider convolutional neural networks operating on sparse
inputs with an application to depth upsampling from sparse laser scan data.
First, we show that traditional convolutional networks perform poorly when
applied to sparse data even when the location of missing data is provided to
the network. To overcome this problem, we propose a simple yet effective sparse
convolution layer which explicitly considers the location of missing data
during the convolution operation. We demonstrate the benefits of the proposed
network architecture in synthetic and real experiments with respect to various
baseline approaches. Compared to dense baselines, the proposed sparse
convolution network generalizes well to novel datasets and is invariant to the
level of sparsity in the data. For our evaluation, we derive a novel dataset
from the KITTI benchmark, comprising 93k depth annotated RGB images. Our
dataset allows for training and evaluating depth upsampling and depth
prediction techniques in challenging real-world settings and will be made
available upon publication
A Novel Weight-Shared Multi-Stage CNN for Scale Robustness
Convolutional neural networks (CNNs) have demonstrated remarkable results in
image classification for benchmark tasks and practical applications. The CNNs
with deeper architectures have achieved even higher performance recently thanks
to their robustness to the parallel shift of objects in images as well as their
numerous parameters and the resulting high expression ability. However, CNNs
have a limited robustness to other geometric transformations such as scaling
and rotation. This limits the performance improvement of the deep CNNs, but
there is no established solution. This study focuses on scale transformation
and proposes a network architecture called the weight-shared multi-stage
network (WSMS-Net), which consists of multiple stages of CNNs. The proposed
WSMS-Net is easily combined with existing deep CNNs such as ResNet and DenseNet
and enables them to acquire robustness to object scaling. Experimental results
on the CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate that existing
deep CNNs combined with the proposed WSMS-Net achieve higher accuracies for
image classification tasks with only a minor increase in the number of
parameters and computation time.Comment: accepted version, 13 page
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