1 research outputs found
A Gray Box Interpretable Visual Debugging Approach for Deep Sequence Learning Model
Deep Learning algorithms are often used as black box type learning and they
are too complex to understand. The widespread usability of Deep Learning
algorithms to solve various machine learning problems demands deep and
transparent understanding of the internal representation as well as decision
making. Moreover, the learning models, trained on sequential data, such as
audio and video data, have intricate internal reasoning process due to their
complex distribution of features. Thus, a visual simulator might be helpful to
trace the internal decision making mechanisms in response to adversarial input
data, and it would help to debug and design appropriate deep learning models.
However, interpreting the internal reasoning of deep learning model is not well
studied in the literature. In this work, we have developed a visual interactive
web application, namely d-DeVIS, which helps to visualize the internal
reasoning of the learning model which is trained on the audio data. The
proposed system allows to perceive the behavior as well as to debug the model
by interactively generating adversarial audio data point. The web application
of d-DeVIS is available at ddevis.herokuapp.com