Abstract
Deep Neural Networks use thousands of mostly incomprehensible features to identify a single class, a decision no human can follow. We propose an interpretable sparse and low dimensional final decision layer in a deep neural network with measurable aspects of interpretability and demonstrate it on fine-grained image classification. We argue that a human can only understand the decision of a machine learning model, if the features are interpretable and only very few of them are used for a single decision. For that matter, the final layer has to be sparse and, to make interpreting the features feasible, low dimensional. We call a model with a Sparse Low-Dimensional Decision SLDD-Model. We show that a SLDD-Model is easier to interpret locally and globally than a dense high-dimensional decision layer while being able to maintain competitive accuracy. Additionally, we propose a loss function that improves a model's feature diversity and accuracy. Our more interpretable SLDD-Model only uses 5 out of just 50 features per class, while maintaining 97% to 100% of the accuracy on four common benchmark datasets compared to the baseline model with 2048 features.
Abstract (translated)
深度神经网络使用成千上万个几乎无法理解的特征来识别一个类别，这是一个人类无法跟随的决定。我们提出了一种可解释的稀疏和低维度的最后决策层，在具有可解释性测量方面的深度神经网络中，并将其应用于精细的图像分类。我们认为，如果特征可解释，而且只有很少几个特征用于一个决定，那么人类只能理解机器学习模型的决定。因此，最终层必须稀疏，并且为了解释特征可行，低维度。我们称之为具有稀疏低维度决策层的SLDD-模型。我们表明，SLDD-模型在 local和global解释方面比Dense高维度决策层更容易实现，同时能够保持竞争精度。此外，我们提出了一个损失函数，以改善模型的特征多样性和精度。我们的更可解释的SLDD-模型仅使用每个类别中的5个特征，而在每个常见基准数据集上保持了97%至100%的精度，相比之下，与具有2048个特征的基线模型相比，我们的SLDD-模型保持了相同的精度。
URL
https://arxiv.org/abs/2303.13166
