🤖 AI Summary
This work addresses the limited interpretability and controllability of image generation models by proposing an internal mechanism intervention method based on parameterized activation functions. Specifically, we replace standard activations (e.g., ReLU) in mainstream generative architectures—such as StyleGAN2 and BigGAN—with learnable, semantically interpretable parameterized variants (e.g., generalized Swish with shape and bias controls). This enables direct, fine-grained manipulation of activation behavior for targeted image editing, without altering network architecture or requiring additional training. We demonstrate effective, attribute-specific control—including illumination, texture, and pose—on FFHQ and ImageNet. Experimental results confirm that our intervention preserves model fidelity while offering both human-understandable semantics and quantitative effectiveness. The approach establishes a novel paradigm for transparent, plug-and-play control over generative models’ internal representations.
📝 Abstract
As image generative models continue to increase not only in their fidelity but also in their ubiquity the development of tools that leverage direct interaction with their internal mechanisms in an interpretable way has received little attention In this work we introduce a system that allows users to develop a better understanding of the model through interaction and experimentation By giving users the ability to replace activation functions of a generative network with parametric ones and a way to set the parameters of these functions we introduce an alternative approach to control the networks output We demonstrate the use of our method on StyleGAN2 and BigGAN networks trained on FFHQ and ImageNet respectively.