ChannelExplorer: Exploring Class Separability Through Activation Channel Visualization.

Deep neural networks (DNNs) achieve state-of-the-art performance in many vision tasks, yet understanding their internal behavior remains challenging, particularly how different layers and activation channels contribute to class separability. We introduce ChannelExplorer, an interactive visual analytics tool for analyzing image-based outputs across model layers, emphasizing data-driven insights over architecture analysis for exploring class separability. ChannelExplorer begins with a dataset-level overview and progressively drills down to individual examples, summarizing activations across layers along the way. It presents these results primarily through three coordinated views: a Scatterplot View to reveal inter and intra-class confusion, a Jaccard Similarity View to quantify activation overlap, and a Heatmap View to inspect activation channel patterns. Our technique supports diverse model architectures, including CNNs, GANs, ResNet, and Stable Diffusion models. We demonstrate the capabilities of ChannelExplorer through four use-case scenarios: (1) generating class hierarchy in ImageNet, (2) finding mislabeled images, (3) identifying activation channel contributions, and (4) locating latent states' position in the Stable Diffusion model. Finally, we evaluate the tool with expert users.