In this paper, we propose a data-free method of extracting Impressions of
each class from the classifier's memory. The Deep Learning regime empowers
classifiers to extract distinct patterns (or features) of a given class from
training data, which is the basis on which they generalize to unseen data.
Before deploying these models on critical applications, it is advantageous to
visualize the features considered to be essential for classification. Existing
visualization methods develop high confidence images consisting of both
background and foreground features. This makes it hard to judge what the
crucial features of a given class are. In this work, we propose a
saliency-driven approach to visualize discriminative features that are
considered most important for a given task. Another drawback of existing
methods is that confidence of the generated visualizations is increased by
creating multiple instances of the given class. We restrict the algorithm to
develop a single object per image, which helps further in extracting features
of high confidence and also results in better visualizations. We further
demonstrate the generation of negative images as naturally fused images of two
or more classes.

该论文提出了一种用于从分类器的内存中提取每个类别的印象的数据无关方法，通过一种基于显著性驱动的可视化方法展示了深度学习分类器在训练数据中提取的判别特征，帮助提高生成图像的置信度。