Search Results for author:
Found 7 papers, 1 papers with code
DensePANet: An improved generative adversarial network for photoacoustic tomography image reconstruction from sparse data
In this study, we proposed an end-to-end method called DensePANet to solve the problem of PAT image reconstruction from sparse data.
Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image
In this paper, a low parameter deep learning framework utilizing the Non-metric Multi-Dimensional scaling (NMDS) method, is proposed to recover the 3D shape of 2D landmarks on a human face, in a single input image.
Survey of Deep Learning Methods for Inverse Problems
In this paper we investigate a variety of deep learning strategies for solving inverse problems.
A New Approach for Optimizing Highly Nonlinear Problems Based on the Observer Effect Concept
A lot of real-world engineering problems represent dynamicity with nests of nonlinearities due to highly complex network of exponential functions or large number of differential equations interacting together.
Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering
Deep generative models are stochastic neural networks capable of learning the distribution of data so as to generate new samples.
Unsupervised Feature Learning Toward a Real-time Vehicle Make and Model Recognition
Here, we formulate the vehicle make and model recognition as a fine-grained classification problem and propose a new configurable on-road vehicle make and model recognition framework.
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking.
