Search Results for author:
Found 17 papers, 8 papers with code
Multidimensional Compressed Sensing for Spectral Light Field Imaging
This paper considers a compressive multi-spectral light field camera model that utilizes a one-hot spectralcoded mask and a microlens array to capture spatial, angular, and spectral information using a single monochrome sensor.
FROST-BRDF: A Fast and Robust Optimal Sampling Technique for BRDF Acquisition
In this paper, we formulate BRDF acquisition as a compressed sensing problem, where the sensing operator is one that performs sub-sampling of the BRDF signal according to a set of optimal sample directions.
Comparison of single image HDR reconstruction methods — the caveats of quality assessment
As the problem of reconstructing high dynamic range (HDR) images from a single exposure has attracted much research effort, it is essential to provide a robust protocol and clear guidelines on how to evaluate and compare new methods.
Standalone Neural ODEs with Sensitivity Analysis
This paper presents the Standalone Neural ODE (sNODE), a continuous-depth neural ODE model capable of describing a full deep neural network.
Learning via nonlinear conjugate gradients and depth-varying neural ODEs
The inverse problem of supervised reconstruction of depth-variable (time-dependent) parameters in a neural ordinary differential equation (NODE) is considered, that means finding the weights of a residual network with time continuous layers.
Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
We bring forward a number of considerations, such as view generation for the contrastive objective and hyper-parameter tuning.
Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection
The scarcity of labeled data is a major bottleneck for developing accurate and robust deep learning-based models for histopathology applications.
How to cheat with metrics in single-image HDR reconstruction
Here, we reproduce a typical evaluation using existing as well as simulated SI-HDR methods to demonstrate how different aspects of the problem affect objective quality metrics.
Ensembles of GANs for synthetic training data generation
This work investigates the use of synthetic images, created by generative adversarial networks (GANs), as the only source of training data.
A Study of Deep Learning Colon Cancer Detection in Limited Data Access Scenarios
One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming.
Classifying the classifier: dissecting the weight space of neural networks
of neural network classifiers, and train a large number of models to represent the weight space.
A Closer Look at Domain Shift for Deep Learning in Histopathology
Most centrally, we present a novel measure for evaluating the distance between domains in the context of the learned representation of a particular model.
Single-frame Regularization for Temporally Stable CNNs
The regularization is formulated to account for different types of motion that can occur between frames, so that temporally stable CNNs can be trained without the need for video material or expensive motion estimation.
Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
We introduce Synscapes -- a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis.
HDR image reconstruction from a single exposure using deep CNNs
We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing.
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks.
A Unified Framework for Multi-Sensor HDR Video Reconstruction
We present an implementation in CUDA and show real-time performance for an experimental 4 Mpixel multi-sensor HDR video system.
