53 research outputs found
AICSD: Adaptive Inter-Class Similarity Distillation for Semantic Segmentation
In recent years, deep neural networks have achieved remarkable accuracy in
computer vision tasks. With inference time being a crucial factor, particularly
in dense prediction tasks such as semantic segmentation, knowledge distillation
has emerged as a successful technique for improving the accuracy of lightweight
student networks. The existing methods often neglect the information in
channels and among different classes. To overcome these limitations, this paper
proposes a novel method called Inter-Class Similarity Distillation (ICSD) for
the purpose of knowledge distillation. The proposed method transfers high-order
relations from the teacher network to the student network by independently
computing intra-class distributions for each class from network outputs. This
is followed by calculating inter-class similarity matrices for distillation
using KL divergence between distributions of each pair of classes. To further
improve the effectiveness of the proposed method, an Adaptive Loss Weighting
(ALW) training strategy is proposed. Unlike existing methods, the ALW strategy
gradually reduces the influence of the teacher network towards the end of
training process to account for errors in teacher's predictions. Extensive
experiments conducted on two well-known datasets for semantic segmentation,
Cityscapes and Pascal VOC 2012, validate the effectiveness of the proposed
method in terms of mIoU and pixel accuracy. The proposed method outperforms
most of existing knowledge distillation methods as demonstrated by both
quantitative and qualitative evaluations. Code is available at:
https://github.com/AmirMansurian/AICSDComment: 10 pages, 5 figures, 5 table
Model-Free Prediction of Adversarial Drop Points in 3D Point Clouds
Adversarial attacks pose serious challenges for deep neural network
(DNN)-based analysis of various input signals. In the case of 3D point clouds,
methods have been developed to identify points that play a key role in the
network decision, and these become crucial in generating existing adversarial
attacks. For example, a saliency map approach is a popular method for
identifying adversarial drop points, whose removal would significantly impact
the network decision. Generally, methods for identifying adversarial points
rely on the deep model itself in order to determine which points are critically
important for the model's decision. This paper aims to provide a novel
viewpoint on this problem, in which adversarial points can be predicted
independently of the model. To this end, we define 14 point cloud features and
use multiple linear regression to examine whether these features can be used
for model-free adversarial point prediction, and which combination of features
is best suited for this purpose. Experiments show that a suitable combination
of features is able to predict adversarial points of three different networks
-- PointNet, PointNet++, and DGCNN -- significantly better than a random guess.
The results also provide further insight into DNNs for point cloud analysis, by
showing which features play key roles in their decision-making process.Comment: 10 pages, 6 figure
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