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Found 29 papers, 11 papers with code
Interpolation between CNNs and ResNets
Although ordinary differential equations (ODEs) provide insights for designing networks architectures, its relationship with the non-residual convolutional neural networks (CNNs) is still unclear.
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder
We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks.
Convection-Diffusion Equation: A Theoretically Certified Framework for Neural Networks
Neural network can be viewed as a map from a simple base model to a complicate function.
Reconstruction of dynamical systems from data without time labels
In this paper, we study the method to reconstruct dynamical systems from data without time labels.
Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles
The single-particle cryo-EM field faces the persistent challenge of preferred orientation, lacking general computational solutions.
An axiomatized PDE model of deep neural networks
Based on the convection-diffusion equation, we design a new training method for ResNets.
Semi-Supervised Clustering via Dynamic Graph Structure Learning
However, existing methods adopt a static affinity matrix to learn the low-dimensional representations of data points and do not optimize the affinity matrix during the learning process.
Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods
Training deep neural networks (DNNs) is an important and challenging optimization problem in machine learning due to its non-convexity and non-separable structure.
A scalable deep learning approach for solving high-dimensional dynamic optimal transport
In this work, we propose a deep learning based method to solve the dynamic optimal transport in high dimensional space.
Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
Current approaches aim at generating synthesized training data from unpaired samples by exploring the relationship between the corrupted and clean data.
Unsupervised Deep Learning Meets Chan-Vese Model
The Chan-Vese (CV) model is a classic region-based method in image segmentation.
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Without accessing to the old training samples, knowledge transfer from the old tasks to each new task is difficult to determine, which might be either positive or negative.
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization
Anderson mixing (AM) is an acceleration method for fixed-point iterations.
A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications
We prove that the basic version of ST-AM is equivalent to the full-memory AM in strongly convex quadratic optimization, and with minor changes it has local linear convergence for solving general nonlinear fixed-point problems.
Learning From Unpaired Data: A Variational Bayes Approach
Collecting the paired training data is a difficult task in practice, but the unpaired samples broadly exist.
Diffusion Mechanism in Residual Neural Network: Theory and Applications
In many learning tasks with limited training samples, the diffusion connects the labeled and unlabeled data points and is a critical component for achieving high classification accuracy.
Layer-wise Adversarial Defense: An ODE Perspective
Deep neural networks are observed to be fragile against adversarial attacks, which have dramatically limited their practical applicability.
An Unsupervised Deep Learning Approach for Real-World Image Denoising
In the real-world case, the noise distribution is so complex that the simplified additive white Gaussian (AWGN) assumption rarely holds, which significantly deteriorates the Gaussian denoisers' performance.
Task-Oriented Feature Distillation
Moreover, an orthogonal loss is applied to the feature resizing layer in TOFD to improve the performance of knowledge distillation.
Interpolation between Residual and Non-Residual Networks
Although ordinary differential equations (ODEs) provide insights for designing network architectures, its relationship with the non-residual convolutional neural networks (CNNs) is still unclear.
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Existing domain adaptation methods aim at learning features that can be generalized among domains.
Ranked #3 on
Domain Adaptation
on USPS-to-MNIST
Exploring Frequency Domain Interpretation of Convolutional Neural Networks
To the best of our knowledge, there is no study on the interpretation of modern CNNs from the perspective of the frequency proportion of filters.
Brain-inspired reverse adversarial examples
On contrast, current state-of-the-art deep learning approaches heavily depend on the variety of training samples and the capacity of the network.
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models
Remarkable achievements have been attained by deep neural networks in various applications.
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
Different from traditional knowledge distillation - a knowledge transformation methodology among networks, which forces student neural networks to approximate the softmax layer outputs of pre-trained teacher neural networks, the proposed self distillation framework distills knowledge within network itself.
Wasserstein-Fisher-Rao Document Distance
As a fundamental problem of natural language processing, it is important to measure the distance between different documents.
Whole Brain Susceptibility Mapping Using Harmonic Incompatibility Removal
Quantitative susceptibility mapping (QSM) aims to visualize the three dimensional susceptibility distribution by solving the field-to-source inverse problem using the phase data in magnetic resonance signal.
Equiangular Kernel Dictionary Learning With Applications to Dynamic Texture Analysis
Most existing dictionary learning algorithms consider a linear sparse model, which often cannot effectively characterize the nonlinear properties present in many types of visual data, e. g. dynamic texture (DT).
l0 Norm Based Dictionary Learning by Proximal Methods with Global Convergence
Sparse coding and dictionary learning have seen their applications in many vision tasks, which usually is formulated as a non-convex optimization problem.
