Search Results for author:
Found 21 papers, 5 papers with code
Sequential Ensembling for Semantic Segmentation
Ensemble approaches for deep-learning-based semantic segmentation remain insufficiently explored despite the proliferation of competitive benchmarks and downstream applications.
Ranked #10 on
Semantic Segmentation
on PASCAL Context
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement
Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution.
Deep Learning with Mixed Supervision for Brain Tumor Segmentation
In this paper, we propose to use both types of training data (fully-annotated and weakly-annotated) to train a deep learning model for segmentation.
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences.
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.
Semi-Supervised Learning via Compact Latent Space Clustering
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation.
Autofocus Layer for Semantic Segmentation
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
Ranked #5 on
Brain Tumor Segmentation
on BRATS-2015
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs).
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain.
Predicting Personal Traits from Facial Images using Convolutional Neural Networks Augmented with Facial Landmark Information
We consider the task of predicting various traits of a person given an image of their face.
Measuring Neural Net Robustness with Constraints
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled.
Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups
We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root.
Refining Architectures of Deep Convolutional Neural Networks
Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks.
Decision Forests, Convolutional Networks and the Models in-Between
We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency.
Deep Neural Decision Forests
We present Deep Neural Decision Forests - a novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner.
Training CNNs with Low-Rank Filters for Efficient Image Classification
Applying our method to a near state-of-the-art network for CIFAR, we achieved comparable accuracy with 46% less compute and 55% fewer parameters.
Filter Forests for Learning Data-Dependent Convolutional Kernels
We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context.
Decision Jungles: Compact and Rich Models for Classification
Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision.
GeoF: Geodesic Forests for Learning Coupled Predictors
This paper presents a new and efficient forest based model that achieves spatially consistent semantic image segmentation by encoding variable dependencies directly in the feature space the forests operate on.
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image.
Context-Sensitive Decision Forests for Object Detection
In this paper we introduce Context-Sensitive Decision Forests - A new perspective to exploit contextual information in the popular decision forest framework for the object detection problem.
