The primary aim of this manuscript is to underscore a significant limitation
in current deep learning models, particularly vision models. Unlike human
vision, which efficiently selects only the essential visual areas for further
processing, leading to high speed and low energy consumption, deep vision
models process the entire image. In this work, we examine this issue from a
broader perspective and propose a solution that could pave the way for the next
generation of more efficient vision models. Basically, convolution and pooling
operations are selectively applied to altered regions, with a change map sent
to subsequent layers. This map indicates which computations need to be
repeated. The code is available at
this https URL

当前深度学习模型在处理图像时存在的重要局限性是它们无法像人类视觉一样，高效地只选择必要的视觉区域进行处理，导致速度慢且能耗高。本文从广泛的角度考察了这个问题，并提出了解决方案，为下一代更高效的视觉模型铺平了道路。基本上，本文采用了选择性地对变换后的区域进行卷积和池化操作，并将变化图传递给后续层次。这个图指示了哪些计算需要重复执行。源代码可在此 URL 进行查看。

解决深度视觉模型中的一个基本限制：缺乏空间注意力

Addressing a fundamental limitation in deep vision models: lack of  spatial attention

We propose a training scheme to train neural network-based source separation
algorithms from scratch when parallel clean data is unavailable. In particular,
we demonstrate that an unsupervised spatial clustering algorithm is sufficient
to guide the training of a deep clustering system. We argue that previous work
on deep clustering requires strong supervision and elaborate on why this is a
limitation. We demonstrate that (a) the single-channel deep clustering system
trained according to the proposed scheme alone is able to achieve a similar
performance as the multi-channel teacher in terms of word error rates and (b)
initializing the spatial clustering approach with the deep clustering result
yields a relative word error rate reduction of 26 % over the unsupervised
teacher.

提出了一种通过无监督的空间聚类算法训练深度聚类系统，从而在没有并行清洁数据时从头训练基于神经网络的源分离算法的方案。