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What Makes a Good Dataset for Knowledge Distillation?
Logan Frank, Jim Davis
Knowledge distillation (KD) has been a popular and effective method for model compression. One important assumption of KD is that the teacher's original dataset will also be availa…
Deep Learning Improvements for Sparse Spatial Field Reconstruction
Robert Sunderhaft, Logan Frank, Jim Davis
Accurately reconstructing a global spatial field from sparse data has been a longstanding problem in several domains, such as Earth Sciences and Fluid Dynamics. Historically, scien…
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images
Logan Frank, Jim Davis
Knowledge distillation (KD) has been a popular and effective method for model compression. One important assumption of KD is that the original training dataset is always available.…
Revisiting Batch Norm Initialization
Jim Davis, Logan Frank
Batch normalization (BN) is comprised of a normalization component followed by an affine transformation and has become essential for training deep neural networks. Standard initial…