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Found 20 papers, 6 papers with code
StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation
We show analytically that as the representation power of the network increases, the optimization approaches a partial differential equation (PDE) in the continuum limit that is unstable.
Surprising Instabilities in Training Deep Networks and a Theoretical Analysis
We discover restrained numerical instabilities in current training practices of deep networks with stochastic gradient descent (SGD).
Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow
Event cameras are novel bio-inspired sensors that measure per-pixel brightness differences asynchronously.
Accelerated PDEs for Construction and Theoretical Analysis of an SGD Extension
We introduce a recently developed framework PDE Acceleration, which is a variational approach to accelerated optimization with partial differential equations (PDE), in the context of optimization of deep networks.
Deep Learning 3D Dose Prediction for Conventional Lung IMRT Using Consistent/Unbiased Automated Plans
Moreover, any changes in the clinical criteria requires a new set of manually generated plans by planners to build a new prediction model.
An Efficiently Coupled Shape and Appearance Prior for Active Contour Segmentation
This paper proposes a novel training model based on shape and appearance features for object segmentation in images and videos.
Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation Using Physics-Based Data Augmentation
Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures.
Verifying the Causes of Adversarial Examples
The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence.
An Adaptive View of Adversarial Robustness from Test-time Smoothing Defense
The safety and robustness of learning-based decision-making systems are under threats from adversarial examples, as imperceptible perturbations can mislead neural networks to completely different outputs.
An Interactive Control Approach to 3D Shape Reconstruction
The ability to accurately reconstruct the 3D facets of a scene is one of the key problems in robotic vision.
PDE Acceleration for Active Contours
Following the seminal work of Nesterov, accelerated optimization methods have been used to powerfully boost the performance of first-order, gradient-based parameter estimation in scenarios where second-order optimization strategies are either inapplicable or impractical.
Variational PDEs for Acceleration on Manifolds and Application to Diffeomorphisms
Our approach evolves an infinite number of particles endowed with mass, represented as a mass density.
PDE Acceleration: A convergence rate analysis and applications to obstacle problems
This paper provides a rigorous convergence rate and complexity analysis for a recently introduced framework, called PDE acceleration, for solving problems in the calculus of variations, and explores applications to obstacle problems.
Numerical Analysis Numerical Analysis Analysis of PDEs Dynamical Systems Optimization and Control 65M06, 35Q93, 65K10, 49K20
Accelerated Optimization in the PDE Framework: Formulations for the Manifold of Diffeomorphisms
We present a new class of optimization methods, valid for any optimization problem setup on the space of diffeomorphisms by generalizing Nesterov accelerated optimization to the manifold of diffeomorphisms.
Towards an Understanding of Neural Networks in Natural-Image Spaces
Two major uncertainties, dataset bias and adversarial examples, prevail in state-of-the-art AI algorithms with deep neural networks.
Accelerated Optimization in the PDE Framework: Formulations for the Active Contour Case
Following the seminal work of Nesterov, accelerated optimization methods have been used to powerfully boost the performance of first-order, gradient-based parameter estimation in scenarios where second-order optimization strategies are either inapplicable or impractical.
Coarse-To-Fine Segmentation With Shape-Tailored Continuum Scale Spaces
We formulate an energy for segmentation that is designed to have preference for segmenting the coarse over fine structure of the image, without smoothing across boundaries of regions.
Shape-Tailored Local Descriptors and Their Application to Segmentation and Tracking
Given a region of arbitrary shape in an image, these descriptors are formed from shape-dependent scale spaces of oriented gradients.
Tracking via Motion Estimation with Physically Motivated Inter-Region Constraints
We illustrate the use of this motion estimation scheme in propagating a segmentation across frames and show that it leads to more accurate segmentation than traditional motion estimation that does not make use of physical constraints.
A compact formula for the derivative of a 3-D rotation in exponential coordinates
We present a compact formula for the derivative of a 3-D rotation matrix with respect to its exponential coordinates.
