Deep Generative Models are frequently used to learn continuous representations of complex data distributions by training on a finite number of samples. For any generative model, including pre-trained foundation models with Diffusion or Transformer architectures, generation performance can significantly vary across the learned data manifold. In this paper, we study the local geometry of the learned manifold and its relationship to generation outcomes for a wide range of generative models, including DDPM, Diffusion Transformer (DiT), and Stable Diffusion 1.4. Building on the theory of continuous piecewise-linear (CPWL) generators, we characterize the local geometry in terms of three geometric descriptors - scaling ($\psi$), rank ($\nu$), and complexity/un-smoothness ($\delta$). We provide quantitative and qualitative evidence showing that for a given latent vector, the local descriptors are indicative of post-generation aesthetics, generation diversity, and memorization by the generative model. Finally, we demonstrate that by training a reward model on the 'local scaling' for Stable Diffusion, we can self-improve both generation aesthetics and diversity using geometry sensitive guidance during denoising. Website: https://imtiazhumayun.github.io/generative_geometry.

Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge devices. However, the performance of KD is affected by the large capacity gap between the teacher and student networks. Recent methods have resorted to a multiple teacher assistant (TA) setting for KD, which sequentially decreases the size of the teacher model to relatively bridge the size gap between these models. This paper proposes a new technique called Curriculum Expert Selection for Knowledge Distillation (CES-KD) to efficiently enhance the learning of a compact student under the capacity gap problem. This technique is built upon the hypothesis that a student network should be guided gradually using stratified teaching curriculum as it learns easy (hard) data samples better and faster from a lower (higher) capacity teacher network. Specifically, our method is a gradual TA-based KD technique that selects a single teacher per input image based on a curriculum driven by the difficulty in classifying the image. In this work, we empirically verify our hypothesis and rigorously experiment with CIFAR-10, CIFAR-100, CINIC-10, and ImageNet datasets and show improved accuracy on VGG-like models, ResNets, and WideResNets architectures.

Still-to-video face recognition (FR) systems for watchlist screening seek to recognize individuals of interest given faces captured over a network of video surveillance cameras. Screening faces against a watchlist is a challenging application because only a limited number of reference stills is available per individual during enrollment, and the appearance of face captures in videos changes from camera to camera, due to variations in illumination, pose, blur, scale, expression and occlusion. In order to improve the robustness of FR systems, several local matching techniques have been proposed that rely on static or dynamic weighting of patches. However, these approaches are not suitable for watchlist screening applications where the capturing conditions vary significantly over different camera fields of view (FoV). In this paper, a new dynamic weighting technique is proposed for weighting facial patches based on video data collected a priori from the specific operational domain (camera FoV) and on image quality assessment. Results obtained on videos from the Chokepoint dataset indicate that the proposed approach can significantly outperform the reference local matching methods because patch weights tend to grow for discriminant facial regions.