Discovering Influential Neuron Path in Vision Transformers

Vision Transformer models exhibit immense power yet remain opaque to human understanding, posing challenges and risks for practical applications. While prior research has attempted to demystify these models through input attribution and neuron role analysis, there's been a notable gap in considering layer-level information and the holistic path of information flow across layers. In this paper, we investigate the significance of influential neuron paths within vision Transformers, which is a path of neurons from the model input to output that impacts the model inference most significantly. We first propose a joint influence measure to assess the contribution of a set of neurons to the model outcome. And we further provide a layer-progressive neuron locating approach that efficiently selects the most influential neuron at each layer trying to discover the crucial neuron path from input to output within the target model. Our experiments demonstrate the superiority of our method finding the most influential neuron path along which the information flows, over the existing baseline solutions. Additionally, the neuron paths have illustrated that vision Transformers exhibit some specific inner working mechanism for processing the visual information within the same image category. We further analyze the key effects of these neurons on the image classification task, showcasing that the found neuron paths have already preserved the model capability on downstream tasks, which may also shed some lights on real-world applications like model pruning. Transformer (V aswani et al., 2017) models in the vision domain, such as supervised Vision Transformers (Dosovitskiy et al., 2021) (ViT) or self-supervised pretrained models (He et al., 2022; Oquab et al., 2023), have showcased remarkable performance in various real-world tasks like image classification (Dosovitskiy et al., 2021) and image synthesis (Peebles & Xie, 2023). However, the inner workings of these vision Transformer models remain elusive, despite their impressive achievements. Understanding the internal mechanisms of vision models is crucial for both research and practical applications. When confronted with the model decision outputs, one may raise some questions that, how is the vision Transformer model processing the input information by layer, and which part of the model is significant to derive the final outcome? Unraveling the synergy within these models is essential for comprehending machine learning systems.