19 citations · 31 across the 9 of their papers we have counts for
11 papers
Normalizing Flow Maps
Francesco M. Ruscio, Timothy James Hitge, Fred Zhangzhi Peng +6
Scalable approaches to learning neural dynamical systems have placed diffusion, flow matching, and few-step generators in flow maps at the forefront of generative modeling. Such st…
Global Transport Couplings for Classifier-Free Guided Flows
Katarina Petrović, Zander W. Blasingame, Danyal Rehman +5
Optimal-transport couplings have been shown to reduce training variance in unconditional flow models, but their role in conditional generation remains unclear. A natural approach c…
Strong Stochastic Flow Maps
Sam McCallum, Zander W. Blasingame, Timothy Herschell +3
Flow and diffusion models generate high-quality samples in many modalities; however, many network evaluations are required during inference due to numerical integration of an under…
Elytra: A Flexible Framework for Securing Large Vision Systems
Richard E. Neddo, Emmanuel Atindama, Zander W. Blasingame +1
Adversarial attacks have emerged as a critical threat to autonomous driving systems. These attacks exploit the underlying neural network, allowing small, almost invisible, perturba…
Rex: A Family of Reversible Exponential (Stochastic) Runge-Kutta Solvers
Zander W. Blasingame, Chen Liu
Deep generative models based on neural differential equations have become state-of-the-art for many generation tasks. These models rely on ODE/SDE solvers that integrate from a pri…
Greed is Good: A Unifying Perspective on Guided Generation
Zander W. Blasingame, Chen Liu
Training-free guided generation is a widely used and powerful technique that allows the end user to exert further control over the generative process of flow/diffusion models. Gene…
AdjointDEIS: Efficient Gradients for Diffusion Models
Zander W. Blasingame, Chen Liu
The optimization of the latents and parameters of diffusion models with respect to some differentiable metric defined on the output of the model is a challenging and complex proble…
The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios
Richard E. Neddo, Zander W. Blasingame, Chen Liu
Face morphing attacks pose an increasing threat to face recognition (FR) systems. A morphed photo contains biometric information from two different subjects to take advantage of vu…
Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs
Zander W. Blasingame, Chen Liu
Morphing attacks are an emerging threat to state-of-the-art Face Recognition (FR) systems, which aim to create a single image that contains the biometric information of multiple id…
Fast-DiM: Towards Fast Diffusion Morphs
Zander W. Blasingame, Chen Liu
Diffusion Morphs (DiM) are a recent state-of-the-art method for creating high quality face morphs; however, they require a high number of network function evaluations (NFE) to crea…
Leveraging Diffusion For Strong and High Quality Face Morphing Attacks
Zander W. Blasingame, Chen Liu
Face morphing attacks seek to deceive a Face Recognition (FR) system by presenting a morphed image consisting of the biometric qualities from two different identities with the aim…