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Found 18 papers, 9 papers with code
Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients
Diagnostic investigation has an important role in risk stratification and clinical decision making of patients with suspected and documented Coronary Artery Disease (CAD).
PMaF: Deep Declarative Layers for Principal Matrix Features
We explore two differentiable deep declarative layers, namely least squares on sphere (LESS) and implicit eigen decomposition (IED), for learning the principal matrix features (PMaF).
Towards Understanding Gradient Approximation in Equality Constrained Deep Declarative Networks
We explore conditions for when the gradient of a deep declarative node can be approximated by ignoring constraint terms and still result in a descent direction for the global loss function.
Aligning Step-by-Step Instructional Diagrams to Video Demonstrations
In this paper, we consider a novel setting where such an alignment is between (i) instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) and (ii) video segments from in-the-wild videos; these videos comprising an enactment of the assembly actions in the real world.
Mutual Exclusive Modulator for Long-Tailed Recognition
In this paper, we try to look into the root cause of the LTR task, i. e., training samples for each class are greatly imbalanced, and propose a straightforward solution.
NeRFEditor: Differentiable Style Decomposition for Full 3D Scene Editing
Specifically, we use a NeRF model to generate numerous image-angle pairs to train an adjustor, which can adjust the StyleGAN latent code to generate high-fidelity stylized images for any given angle.
3D Brain and Heart Volume Generative Models: A Survey
Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability.
Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images
Novel 3D network architectures are proposed for both the generator and discriminator of the GAN model to significantly reduce the number of parameters while maintaining the quality of image generation.
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection
Empirically, DERM outperformed the state-of-the-art on the unsupervised AD benchmark consisting of 18 datasets.
Feature-Robust Optimal Transport for High-Dimensional Data
To show the effectiveness of FROT, we propose using the FROT algorithm for the layer selection problem in deep neural networks for semantic correspondence.
A Multi-Mode Modulator for Multi-Domain Few-Shot Classification
Most existing few-shot classification methods only consider generalization on one dataset (i. e., single-domain), failing to transfer across various seen and unseen domains.
Semantic Correspondence as an Optimal Transport Problem
Establishing dense correspondences across semantically similar images is a challenging task.
Ranked #6 on
Semantic correspondence
on PF-WILLOW
Feature Robust Optimal Transport for High-dimensional Data
To show the effectiveness of FROT, we propose using the FROT algorithm for the layer selection problem in deep neural networks for semantic correspondence.
LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport
To estimate the mutual information from data, a common practice is preparing a set of paired samples $\{(\mathbf{x}_i,\mathbf{y}_i)\}_{i=1}^n \stackrel{\mathrm{i. i. d.
MxML: Mixture of Meta-Learners for Few-Shot Classification
A meta-model is trained on a distribution of similar tasks such that it learns an algorithm that can quickly adapt to a novel task with only a handful of labeled examples.
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
UTS submission to Google YouTube-8M Challenge 2017
In this paper, we present our solution to Google YouTube-8M Video Classification Challenge 2017.
Pooling the Convolutional Layers in Deep ConvNets for Action Recognition
It achieves the accuracy of 93. 78\% on UCF101 which is the state-of-the-art and the accuracy of 65. 62\% on HMDB51 which is comparable to the state-of-the-art.
