Deep Neural Networks have often been called the black box because of the
complex, deep architecture and non-transparency presented by the inner layers.
There is a lack of trust to use Artificial Intelligence in critical and
high-precision fields such as security, finance, health, and manufacturing
industries. A lot of focused work has been done to provide interpretable
models, intending to deliver meaningful insights into the thoughts and behavior
of neural networks. In our research, we compare the state-of-the-art methods in
the Activation-based methods (ABM) for interpreting predictions of CNN models,
specifically in the application of Image Classification. We then extend the
same for eight CNN-based architectures to compare the differences in
visualization and thus interpretability. We introduced a novel technique
Feature CAM, which falls in the perturbation-activation combination, to create
fine-grained, class-discriminative visualizations. The resulting saliency maps
from our experiments proved to be 3-4 times better human interpretable than the
state-of-the-art in ABM. At the same time it reserves machine interpretability,
which is the average confidence scores in classification.

我们的研究比较了应用于图像分类的激活方法（ABM）中最先进的方法，并通过引入新技术 Feature CAM，得到了在可解释性方面比现有技术提高 3-4 倍的结果。

特征 CAM：解释性图像分类中的人工智能

Feature CAM: Interpretable AI in Image Classification

For the gradient computation across the time domain in Spiking Neural
Networks (SNNs) training, two different approaches have been independently
studied. The first is to compute the gradients with respect to the change in
spike activation (activation-based methods), and the second is to compute the
gradients with respect to the change in spike timing (timing-based methods). In
this work, we present a comparative study of the two methods and propose a new
supervised learning method that combines them. The proposed method utilizes
each individual spike more effectively by shifting spike timings as in the
timing-based methods as well as generating and removing spikes as in the
activation-based methods. Experimental results showed that the proposed method
achieves higher performance in terms of both accuracy and efficiency than the
previous approaches.

在 Spiking Neural Networks （SNNs）训练的时间域梯度计算中，独立研究了两种方法：一种是计算与峰值激活变化相关的梯度（激活方法），另一种是计算与峰值时序变化相关的梯度（时序方法）。我们提出了一种将两种方法结合的新的监督式学习方法。实验结果表明，该方法在准确性和效率方面都比先前的方法表现更好。