
PDOeConvs: Partial Differential Operator Based Equivariant Convolutions
Recent research has shown that incorporating equivariance into neural ne...
read it

Improving Semantic Segmentation via Decoupled Body and Edge Supervision
Existing semantic segmentation approaches either aim to improve the obje...
read it

MaximumandConcatenation Networks
While successful in many fields, deep neural networks (DNNs) still suffe...
read it

Classify and Generate Reciprocally: Simultaneous PositiveUnlabelled Learning and Conditional Generation with Extra Data
The scarcity of classlabeled data is a ubiquitous bottleneck in a wide ...
read it

Invertible Image Rescaling
Highresolution digital images are usually downscaled to fit various dis...
read it

Spatial Pyramid Based Graph Reasoning for Semantic Segmentation
The convolution operation suffers from a limited receptive filed, while ...
read it

Revisiting EXTRA for Smooth Distributed Optimization
EXTRA is a popular method for the dencentralized distributed optimizatio...
read it

Histogram Transform Ensembles for Largescale Regression
We propose a novel algorithm for largescale regression problems named h...
read it

Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
The correspondence between residual networks and dynamical systems motiv...
read it

Patchlevel Neighborhood Interpolation: A General and Effective Graphbased Regularization Strategy
Regularization plays a crucial role in machine learning models, especial...
read it

SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
The panoptic segmentation task requires a unified result from semantic a...
read it

Tensor QRank: A New Data Dependent Tensor Rank
Recently, the Tensor Nuclear Norm (TNN) regularization based on tSVD ha...
read it

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models
The design of deep graph models still remains to be investigated and the...
read it

ExpectationMaximization Attention Networks for Semantic Segmentation
Selfattention mechanism has been widely used for various tasks. It is d...
read it

ADATucker: Compressing Deep Neural Networks via Adaptive Dimension Adjustment Tucker Decomposition
Despite the recent success of deep learning models in numerous applicati...
read it

Differentiable Linearized ADMM
Recently, a number of learningbased optimization methods that combine d...
read it

SelfSupervised Convolutional Subspace Clustering Network
Subspace clustering methods based on data selfexpression have become ve...
read it

Deep Comprehensive Correlation Mining for Image Clustering
Recent developed deep unsupervised methods allow us to jointly learn rep...
read it

Virtual Adversarial Training on Graph Convolutional Networks in Node Classification
The effectiveness of Graph Convolutional Networks (GCNs) has been demons...
read it

MultiStage SelfSupervised Learning for Graph Convolutional Networks
Graph Convolutional Networks(GCNs) play a crucial role in graph learning...
read it

Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN
Deep neural networks have been widely deployed in various machine learni...
read it

Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors
Most previous works usually explained adversarial examples from several ...
read it

Sharp Analysis for Nonconvex SGD Escaping from Saddle Points
In this paper, we prove that the simplest Stochastic Gradient Descent (S...
read it

Lifted Proximal Operator Machines
We propose a new optimization method for training feedforward neural ne...
read it

Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications
The heavytailed distributions of corrupted outliers and singular values...
read it

Optimization Algorithm Inspired Deep Neural Network Structure Design
Deep neural networks have been one of the dominant machine learning appr...
read it

Joint Subbands Learning with Clique Structures for Wavelet Domain SuperResolution
Convolutional neural networks (CNNs) have recently achieved great succes...
read it

On the Convergence of Learningbased Iterative Methods for Nonconvex Inverse Problems
Numerous tasks at the core of statistics, learning and vision areas are ...
read it

Recurrent SqueezeandExcitation Context Aggregation Net for Single Image Deraining
Rain streaks can severely degrade the visibility, which causes many curr...
read it

Essential Tensor Learning for Multiview Spectral Clustering
Multiview clustering attracts much attention recently, which aims to ta...
read it

SPIDER: NearOptimal NonConvex Optimization via Stochastic Path Integrated Differential Estimator
In this paper, we propose a new technique named Stochastic PathIntegrat...
read it

Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements
The recent proposed Tensor Nuclear Norm (TNN) [Lu et al., 2016; 2018a] i...
read it

Subspace Clustering by Block Diagonal Representation
This paper studies the subspace clustering problem. Given some data poin...
read it

Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
In this paper, we consider the Tensor Robust Principal Component Analysi...
read it

Convolutional Neural Networks with Alternately Updated Clique
Improving information flow in deep networks helps to ease the training d...
read it

Accelerating Asynchronous Algorithms for Convex Optimization by Momentum Compensation
Asynchronous algorithms have attracted much attention recently due to th...
read it

Alternating Multibit Quantization for Recurrent Neural Networks
Recurrent neural networks have achieved excellent performance in many ap...
read it

Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis
Spectral Clustering (SC) is a widely used data clustering method which f...
read it

Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted LowRank Tensors via Convex Optimization
This paper studies the Tensor Robust Principal Component (TRPCA) problem...
read it

Globally VarianceConstrained Sparse Representation for Image Set Compression
Sparse representation presents an efficient approach to approximately re...
read it

Graph Construction with Label Information for SemiSupervised Learning
In the literature, most existing graphbased semisupervised learning (S...
read it

Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors
Annotating images with tags is useful for indexing and retrieving images...
read it

Tensor Sparse and LowRank based Submodule Clustering Method for Multiway Data
A new submodule clustering method via sparse and lowrank representation...
read it

Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Learning deeper convolutional neural networks becomes a tendency in rece...
read it

Completing LowRank Matrices with Corrupted Samples from Few Coefficients in General Basis
Subspace recovery from corrupted and missing data is crucial for various...
read it

Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion
Tagbased image retrieval (TBIR) has drawn much attention in recent year...
read it

Correntropy Induced L2 Graph for Robust Subspace Clustering
In this paper, we study the robust subspace clustering problem, which ai...
read it

Correlation Adaptive Subspace Segmentation by Trace Lasso
This paper studies the subspace segmentation problem. Given a set of dat...
read it

Generalized Singular Value Thresholding
This work studies the Generalized Singular Value Thresholding (GSVT) ope...
read it

Constructing a NonNegative Low Rank and Sparse Graph with DataAdaptive Features
This paper aims at constructing a good graph for discovering intrinsic d...
read it