As deep neural networks include a high number of parameters and operations,
it can be a challenge to implement these models on devices with limited
computational resources. Despite the development of novel pruning methods
toward resource-efficient models, it has become evident that these models are
not capable of handling "imbalanced" and "limited number of data points". With
input and output information, along with the values of the filters, a novel
filter pruning method is proposed. Our pruning method considers the fact that
all information about the importance of a filter may not be reflected in the
value of the filter. Instead, it is reflected in the changes made to the data
after the filter is applied to it. In this work, three methods are compared
with the same training conditions except for the ranking of each method. We
demonstrated that our model performed significantly better than other methods
for medical datasets which are inherently imbalanced. When we removed up to 58%
of FLOPs for the IDRID dataset and up to 45% for the ISIC dataset, our model
was able to yield an equivalent (or even superior) result to the baseline model
while other models were unable to achieve similar results. To evaluate FLOP and
parameter reduction using our model in real-world settings, we built a
smartphone app, where we demonstrated a reduction of up to 79% in memory usage
and 72% in prediction time. All codes and parameters for training different
models are available at this https URL

这篇论文提出了一种新的滤波器剪枝方法，它能够有效地减少深度神经网络所需要的计算资源，同时通过与其他方法的比较和在医学数据集和智能手机应用程序上尝试验证，表明本文提出的方法表现得更好。

Beta-Rank: 一种用于不平衡医学图像分析的强大卷积滤波器剪枝方法

Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced  Medical Image Analysis

We describe the development of a real-time smartphone app that allows the
user to digitize paper receipts in a novel way by "waving" their phone over the
receipts and letting the app automatically detect and rectify the receipts for
subsequent text recognition.
We show that traditional computer vision algorithms for edge and corner
detection do not robustly detect the non-linear and discontinuous edges and
corners of a typical paper receipt in real-world settings. This is particularly
the case when the colors of the receipt and background are similar, or where
other interfering rectangular objects are present. Inaccurate detection of a
receipt's corner positions then results in distorted images when using an
affine projective transformation to rectify the perspective.
We propose an innovative solution to receipt corner detection by treating
each of the four corners as a unique "object", and training a Single Shot
Detection MobileNet object detection model. We use a small amount of real data
and a large amount of automatically generated synthetic data that is designed
to be similar to real-world imaging scenarios.
We show that our proposed method robustly detects the four corners of a
receipt, giving a receipt detection accuracy of 85.3% on real-world data,
compared to only 36.9% with a traditional edge detection-based approach. Our
method works even when the color of the receipt is virtually indistinguishable
from the background.
Moreover, our method is trained to detect only the corners of the central
target receipt and implicitly learns to ignore other receipts, and other
rectangular objects. Including synthetic data allows us to train an even better
model. These factors are a major advantage over traditional edge
detection-based approaches, allowing us to deliver a much better experience to
the user.

本文介绍了一种使用智能手机应用程序实现纸质收据数字化的全新方法，并提出了利用单次射击检测 MobileNet 目标检测模型来可靠地检测纸质收据四个角落位置的创新解决方案。