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Found 12 papers, 3 papers with code
Coloring Deep CNN Layers with Activation Hue Loss
This paper proposes a novel hue-like angular parameter to model the structure of deep convolutional neural network (CNN) activation space, referred to as the {\em activation hue}, for the purpose of regularizing models for more effective learning.
Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture
To fully understand this problem, we analyse the performance of models independently trained with each pooling configurations on CIFAR10, using a ResNet20 network, and show that the position of the downsampling layers can highly influence the performance of a network and predefined downsampling configurations are not optimal.
Ranked #1 on
Neural Architecture Search
on Food-101
(Accuracy (% ) metric)
PSDNet: Determination of Particle Size Distributions Using Synthetic Soil Images and Convolutional Neural Networks
This project aimed to determine the grain size distribution of granular materials from images using convolutional neural networks.
U(1) Symmetry-breaking Observed in Generic CNN Bottleneck Layers
A 3D space $(x, y, t)$ is defined by $(x, y)$ coordinates in the image plane and CNN layer $t$, where a principal ray $(0, 0, t)$ runs in the direction of information propagation through both the optical axis and the image center pixel located at $(x, y)=(0, 0)$, about which the sharpest possible spatial focus is limited to a circle of confusion in the image plane.
Registering Image Volumes using 3D SIFT and Discrete SP-Symmetry
Augmenting local feature properties with sign in addition to standard (location, scale, orientation) geometry leads to descriptors that are invariant to coordinate reflections and intensity contrast inversion.
GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast Descriptor
This work details a highly efficient implementation of the 3D scale-invariant feature transform (SIFT) algorithm, for the purpose of machine learning from large sets of volumetric medical image data.
Curating Subject ID Labels using Keypoint Signatures
Subject ID labels are unique, anonymized codes that can be used to group all images of a subject while maintaining anonymity.
Efficient Pairwise Neuroimage Analysis using the Soft Jaccard Index and 3D Keypoint Sets
Our measure generalizes the Jaccard index to account for soft set equivalence (SSE) between keypoint elements, via an adaptive kernel framework modeling uncertainty in keypoint appearance and geometry.
A Keypoint-based Morphological Signature for Large-scale Neuroimage Analysis
We present an image keypoint-based morphological signature that can be used to efficiently assess the pair-wise whole-brain similarity for large MRI datasets.
Keypoint Transfer for Fast Whole-Body Segmentation
We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images.
On the Applicability of Registration Uncertainty
For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters.
Modeling Information Flow Through Deep Neural Networks
This paper proposes a principled information theoretic analysis of classification for deep neural network structures, e. g. convolutional neural networks (CNN).
