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Found 140 papers, 46 papers with code
Boosted Histogram Transform for Regression
In this paper, we propose a boosting algorithm for regression problems called \textit{boosted histogram transform for regression} (BHTR) based on histogram transforms composed of random rotations, stretchings, and translations.
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability
By viewing ResNet as an explicit Euler discretization of an ordinary differential equation (ODE), for the first time, we find that the adversarial robustness of ResNet is connected to the numerical stability of the corresponding dynamic system.
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models
We also consider the ensemble of code prompting and CoT prompting to combine the strengths of both.
Policy Representation via Diffusion Probability Model for Reinforcement Learning
Popular reinforcement learning (RL) algorithms tend to produce a unimodal policy distribution, which weakens the expressiveness of complicated policy and decays the ability of exploration.
Visual Tuning
With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer.
LION: Implicit Vision Prompt Tuning
Despite recent competitive performance across a range of vision tasks, vision Transformers still have an issue of heavy computational costs.
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
In recent years, contrastive learning achieves impressive results on self-supervised visual representation learning, but there still lacks a rigorous understanding of its learning dynamics.
Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium
We consider the general nonconvex nonconcave minimax problem over continuous variables.
Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks
In particular, our method achieves state-of-the-art accuracy on ImageNet, while the memory cost and training time are reduced by more than 70% and 50%, respectively, compared with BPTT.
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning
In this paper, we deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse, which reveals that the last-layer features of the same class will collapse into a vertex, and the vertices of all classes are aligned with the classifier prototypes, which are formed as a simplex equiangular tight frame (ETF).
class-incremental learning
Few-Shot Class-Incremental Learning
+1
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning
In this paper, we deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse, which reveals that the last-layer features of the same class will collapse into a vertex, and the vertices of all classes are aligned with the classifier prototypes, which are formed as a simplex equiangular tight frame (ETF).
Ranked #2 on
Few-Shot Class-Incremental Learning
on CUB-200-2011
(Average Accuracy metric)
class-incremental learning
Few-Shot Class-Incremental Learning
+1
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks
In this paper, we study spike-based implicit differentiation on the equilibrium state (SPIDE) that extends the recently proposed training method, implicit differentiation on the equilibrium state (IDE), for supervised learning with purely spike-based computation, which demonstrates the potential for energy-efficient training of SNNs.
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation
Third, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross-attention scheme to boost part segmentation qualities further.
Towards Theoretically Inspired Neural Initialization Optimization
With NIO, we improve the classification performance of a variety of neural architectures on CIFAR-10, CIFAR-100, and ImageNet.
Online Training Through Time for Spiking Neural Networks
With OTTT, it is the first time that two mainstream supervised SNN training methods, BPTT with SG and spike representation-based training, are connected, and meanwhile in a biologically plausible form.
Invertible Rescaling Network and Its Extensions
To be specific, we develop invertible models to generate valid degraded images and meanwhile transform the distribution of lost contents to the fixed distribution of a latent variable during the forward degradation.
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption
However, evaluation of knowledge graph completion (KGC) models often ignores the incompleteness -- facts in the test set are ranked against all unknown triplets which may contain a large number of missing facts not included in the KG yet.
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point.
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs
Steerable models can provide very general and flexible equivariance by formulating equivariance requirements in the language of representation theory and feature fields, which has been recognized to be effective for many vision tasks.
Optimization-Induced Graph Implicit Nonlinear Diffusion
Moreover, we show that the optimization-induced variants of our models can boost the performance and improve training stability and efficiency as well.
Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction
As pointed out by previous works, this two-step procedure results in low discriminating power, as 1-WL-GNNs by nature learn node-level representations instead of link-level.
Global Convergence of Over-parameterized Deep Equilibrium Models
A deep equilibrium model (DEQ) is implicitly defined through an equilibrium point of an infinite-depth weight-tied model with an input-injection.
SymNMF-Net for The Symmetric NMF Problem
Furthermore, we analyze the constraints of the inversion layer to ensure the output stability of the network to a certain extent.
Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
In this paper, we propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance that is competitive to ANNs yet with low latency.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap
Our theory suggests an alternative understanding of contrastive learning: the role of aligning positive samples is more like a surrogate task than an ultimate goal, and the overlapped augmented views (i. e., the chaos) create a ladder for contrastive learning to gradually learn class-separated representations.
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training
On the other hand, our unified framework can be extended to the unsupervised scenario, which interprets unsupervised contrastive learning as an important sampling of CEM.
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Modern deep neural networks for classification usually jointly learn a backbone for representation and a linear classifier to output the logit of each class.
Ranked #21 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the $O(ε^{-7/4})$ Complexity
They do not invoke negative curvature exploitation or minimization of regularized surrogate functions as the subroutines.
Gauge Equivariant Transformer
Attention mechanism has shown great performance and efficiency in a lot of deep learning models, in which relative position encoding plays a crucial role.
Efficient Equivariant Network
Group equivariant CNNs (G-CNNs) that incorporate more equivariance can significantly improve the performance of conventional CNNs.
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness
Adversarial robustness, which mainly contains sensitivity-based robustness and spatial robustness, plays an integral part in the robust generalization.
Residual Relaxation for Multi-view Representation Learning
Multi-view methods learn representations by aligning multiple views of the same image and their performance largely depends on the choice of data augmentation.
Optimization inspired Multi-Branch Equilibrium Models
Works have shown the strong connections between some implicit models and optimization problems.
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
In this work, we consider feedback spiking neural networks, which are more brain-like, and propose a novel training method that does not rely on the exact reverse of the forward computation.
Chaos is a Ladder: A New Understanding of Contrastive Learning
Our work suggests an alternative understanding of contrastive learning: the role of aligning positive samples is more like a surrogate task than an ultimate goal, and it is the overlapping augmented views (i. e., the chaos) that create a ladder for contrastive learning to gradually learn class-separated representations.
Is Attention Better Than Matrix Decomposition?
As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery.
Ranked #7 on
Semantic Segmentation
on PASCAL VOC 2012 test
Under-bagging Nearest Neighbors for Imbalanced Classification
In this paper, we propose an ensemble learning algorithm called \textit{under-bagging $k$-nearest neighbors} (\textit{under-bagging $k$-NN}) for imbalanced classification problems.
Reparameterized Sampling for Generative Adversarial Networks
Recently, sampling methods have been successfully applied to enhance the sample quality of Generative Adversarial Networks (GANs).
Demystifying Adversarial Training via A Unified Probabilistic Framework
Based on these, we propose principled adversarial sampling algorithms in both supervised and unsupervised scenarios.
Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
The training of deep neural networks (DNNs) always requires intensive resources for both computation and data storage.
GBHT: Gradient Boosting Histogram Transform for Density Estimation
In this paper, we propose a density estimation algorithm called \textit{Gradient Boosting Histogram Transform} (GBHT), where we adopt the \textit{Negative Log Likelihood} as the loss function to make the boosting procedure available for the unsupervised tasks.
Leveraged Weighted Loss for Partial Label Learning
As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true.
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression
In this paper, we propose a gradient boosting algorithm for large-scale regression problems called \textit{Gradient Boosted Binary Histogram Ensemble} (GBBHE) based on binary histogram partition and ensemble learning.
Optimization Induced Equilibrium Networks
In this paper, we investigate an emerging question: can an implicit equilibrium model's equilibrium point be regarded as the solution of an optimization problem?
BoundarySqueeze: Image Segmentation as Boundary Squeezing
This module is used to squeeze the object boundary from both inner and outer directions, which contributes to precise mask representation.
PDO-eS2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs
Spherical signals exist in many applications, e. g., planetary data, LiDAR scans and digitalization of 3D objects, calling for models that can process spherical data effectively.
Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization
We prove the $O((\frac{\gamma}{1-\sigma_{\gamma}})^2\sqrt{\frac{L}{\epsilon}})$ and $O((\frac{\gamma}{1-\sigma_{\gamma}})^{1. 5}\sqrt{\frac{L}{\mu}}\log\frac{1}{\epsilon})$ complexities for the practical single loop accelerated gradient tracking over time-varying graphs when the problems are nonstrongly convex and strongly convex, respectively, where $\gamma$ and $\sigma_{\gamma}$ are two common constants charactering the network connectivity, $\epsilon$ is the desired precision, and $L$ and $\mu$ are the smoothness and strong convexity constants, respectively.
Graph Contrastive Clustering
On the other hand, a novel graph-based contrastive learning strategy is proposed to learn more compact clustering assignments.
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have attracted more and more attentions in recent years.
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond
Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community.
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search
Our method enables differentiable sparsification, and keeps the derived architecture equivalent to that of Engine-cell, which further improves the consistency between search and evaluation.
Efficient Sampling for Generative Adversarial Networks with Coupling Markov Chains
Recently, sampling methods have been successfully applied to enhance the sample quality of Generative Adversarial Networks (GANs).
EnTranNAS: Towards Closing the Gap between the Architectures in Search and Evaluation
The Engine-cell is differentiable for architecture search, while the Transit-cell only transits the current sub-graph by architecture derivation.
Fast and Differentiable Matrix Inverse and Its Extension to SVD
First of all, to perform matrix inverse, we provide a differentiable yet efficient way, named LD-Minv, which is a learnable deep neural network (DNN) with each layer being an $L$-th order matrix polynomial.
OT-LLP: Optimal Transport for Learning from Label Proportions
Learning from label proportions (LLP), where the training data are arranged in form of groups with only label proportions provided instead of the exact labels, is an important weakly supervised learning paradigm in machine learning.
Learning Optimization-inspired Image Propagation with Control Mechanisms and Architecture Augmentations for Low-level Vision
In recent years, building deep learning models from optimization perspectives has becoming a promising direction for solving low-level vision problems.
Towards Efficient Scene Understanding via Squeeze Reasoning
Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector and perform reasoning within the single vector where the computation cost can be significantly reduced.
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
By doing so, our network for search at each update satisfies the sparsity constraint and is efficient to train.
Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization
Our stochastic gradient computation complexities are the same as the ones of single-machine VR methods, such as SAG, SAGA, and SVRG, and our communication complexities keep the same as those of EXTRA and DIGing, respectively.
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
In implementation, we discretize the system using the numerical schemes of PDOs, deriving approximately equivariant convolutions (PDO-eConvs).
Ranked #1 on
Image Classification
on MNIST-rot-12
Improving Semantic Segmentation via Decoupled Body and Edge Supervision
Our insight is that appealing performance of semantic segmentation requires \textit{explicitly} modeling the object \textit{body} and \textit{edge}, which correspond to the high and low frequency of the image.
Maximum-and-Concatenation Networks
While successful in many fields, deep neural networks (DNNs) still suffer from some open problems such as bad local minima and unsatisfactory generalization performance.
Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data
In particular, we present a novel training framework to jointly target both PU classification and conditional generation when exposing to extra data, especially out-of-distribution unlabeled data, by exploring the interplay between them: 1) enhancing the performance of PU classifiers with the assistance of a novel Conditional Generative Adversarial Network~(CGAN) that is robust to noisy labels, 2) leveraging extra data with predicted labels from a PU classifier to help the generation.
Invertible Image Rescaling
High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images.
Spatial Pyramid Based Graph Reasoning for Semantic Segmentation
The convolution operation suffers from a limited receptive filed, while global modeling is fundamental to dense prediction tasks, such as semantic segmentation.
Revisiting EXTRA for Smooth Distributed Optimization
EXTRA is a popular method for dencentralized distributed optimization and has broad applications.
Histogram Transform Ensembles for Large-scale Regression
Instead, we apply kernel histogram transforms (KHT) equipped with smoother regressors such as support vector machines (SVMs), and it turns out that both single and ensemble KHT enjoy almost optimal convergence rates.
Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
We establish a stability condition for ResNets with step sizes and weight parameters, and point out the effects of step sizes on the stability and performance.
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy
Furthermore, by explicitly constructing a patch-level graph in the different network layers and interpolating the neighborhood features to refine the representation of the current sample, our Patch-level Neighborhood Interpolation can then be applied to enhance two popular regularization strategies, namely Virtual Adversarial Training (VAT) and MixUp, yielding their neighborhood versions.
SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
In order to overcome the lack of supervision, we introduce a differentiable module to resolve the overlap between any pair of instances.
Ranked #8 on
Panoptic Segmentation
on Cityscapes test
Tensor Q-Rank: New Data Dependent Definition of Tensor Rank
Recently, the \textit{Tensor Nuclear Norm~(TNN)} regularization based on t-SVD has been widely used in various low tubal-rank tensor recovery tasks.
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models
The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way.
Ranked #2 on
Node Classification
on MS ACADEMIC
Expectation-Maximization Attention Networks for Semantic Segmentation
It is designed to compute the representation of each position by a weighted sum of the features at all positions.
Ranked #14 on
Semantic Segmentation
on COCO-Stuff test
ADA-Tucker: Compressing Deep Neural Networks via Adaptive Dimension Adjustment Tucker Decomposition
Furthermore, we propose that weight tensors in networks with proper order and balanced dimension are easier to be compressed.
Differentiable Linearized ADMM
Recently, a number of learning-based optimization methods that combine data-driven architectures with the classical optimization algorithms have been proposed and explored, showing superior empirical performance in solving various ill-posed inverse problems, but there is still a scarcity of rigorous analysis about the convergence behaviors of learning-based optimization.
Self-Supervised Convolutional Subspace Clustering Network
However, the applicability of subspace clustering has been limited because practical visual data in raw form do not necessarily lie in such linear subspaces.
Ranked #2 on
Image Clustering
on Extended Yale-B
Deep Comprehensive Correlation Mining for Image Clustering
Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data.
Ranked #7 on
Image Clustering
on Tiny-ImageNet
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN
In this work, we propose a novel defense mechanism called Boundary Conditional GAN to enhance the robustness of deep neural networks against adversarial examples.
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised(M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes.
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification
The effectiveness of Graph Convolutional Networks (GCNs) has been demonstrated in a wide range of graph-based machine learning tasks.
Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors
In this paper, we present a systematic study on adversarial examples from three aspects: the amount of training data, task-dependent and model-specific factors.
Sharp Analysis for Nonconvex SGD Escaping from Saddle Points
In this paper, we give a sharp analysis for Stochastic Gradient Descent (SGD) and prove that SGD is able to efficiently escape from saddle points and find an $(\epsilon, O(\epsilon^{0. 5}))$-approximate second-order stationary point in $\tilde{O}(\epsilon^{-3. 5})$ stochastic gradient computations for generic nonconvex optimization problems, when the objective function satisfies gradient-Lipschitz, Hessian-Lipschitz, and dispersive noise assumptions.
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator
Specially, we prove that the SPIDER-SFO algorithm achieves a gradient computation cost of $\mathcal{O}\left( \min( n^{1/2} \epsilon^{-2}, \epsilon^{-3} ) \right)$ to find an $\epsilon$-approximate first-order stationary point.
Lifted Proximal Operator Machines
LPOM is block multi-convex in all layer-wise weights and activations.
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications
The heavy-tailed distributions of corrupted outliers and singular values of all channels in low-level vision have proven effective priors for many applications such as background modeling, photometric stereo and image alignment.
Optimization Algorithm Inspired Deep Neural Network Structure Design
In this paper, we propose the hypothesis that the neural network structure design can be inspired by optimization algorithms and a faster optimization algorithm may lead to a better neural network structure.
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution
To solve these problems, we propose the Super-Resolution CliqueNet (SRCliqueNet) to reconstruct the high resolution (HR) image with better textural details in the wavelet domain.
On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems
Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague.
Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining
In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers.
Ranked #7 on
Single Image Deraining
on Test2800
Essential Tensor Learning for Multi-view Spectral Clustering
In this paper, we focus on the Markov chain based spectral clustering method and propose a novel essential tensor learning method to explore the high order correlations for multi-view representation.
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator
For stochastic first-order method, combining SPIDER with normalized gradient descent, we propose two new algorithms, namely SPIDER-SFO and SPIDER-SFO\textsuperscript{+}, that solve non-convex stochastic optimization problems using stochastic gradients only.
Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements
Specifically, we show that by solving a TNN minimization problem, the underlying tensor of size $n_1\times n_2\times n_3$ with tubal rank $r$ can be exactly recovered when the given number of Gaussian measurements is $O(r(n_1+n_2-r)n_3)$.
Subspace Clustering by Block Diagonal Representation
Second, we observe that many existing methods approximate the block diagonal representation matrix by using different structure priors, e. g., sparsity and low-rankness, which are indirect.
Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
Equipped with the new tensor nuclear norm, we then solve the TRPCA problem by solving a convex program and provide the theoretical guarantee for the exact recovery.
Convolutional Neural Networks with Alternately Updated Clique
In contrast to prior networks, there are both forward and backward connections between any two layers in the same block.
Accelerating Asynchronous Algorithms for Convex Optimization by Momentum Compensation
$O(1/\epsilon)$) convergence rate for non-strongly convex functions, and $O(\sqrt{\kappa}\log(1/\epsilon))$ (v. s.
Alternating Multi-bit Quantization for Recurrent Neural Networks
Recurrent neural networks have achieved excellent performance in many applications.
Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis
Experimental analysis on several real data sets verifies the effectiveness of our method.
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
In this work, we prove that under certain suitable assumptions, we can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the $\ell_1$-norm, i. e., $\min_{{\mathcal{L}},\ {\mathcal{E}}} \ \|{{\mathcal{L}}}\|_*+\lambda\|{{\mathcal{E}}}\|_1, \ \text{s. t.}
A Unified Convex Surrogate for the Schatten-$p$ Norm
In this paper, we show that for any $p$, $p_1$, and $p_2 >0$ satisfying $1/p=1/p_1+1/p_2$, there is an equivalence between the Schatten-$p$ norm of one matrix and the Schatten-$p_1$ and the Schatten-$p_2$ norms of its two factor matrices.
Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding
Therefore, introducing an accurate rate-constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation.
Graph Construction with Label Information for Semi-Supervised Learning
In the literature, most existing graph-based semi-supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph.
Robust Kernel Estimation With Outliers Handling for Image Deblurring
Estimating blur kernels from real world images is a challenging problem as the linear image formation assumption does not hold when significant outliers, such as saturated pixels and non-Gaussian noise, are present.
Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors
Annotating images with tags is useful for indexing and retrieving images.
Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data
A new submodule clustering method via sparse and low-rank representation for multi-way data is proposed in this paper.
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Learning deeper convolutional neural networks becomes a tendency in recent years.
Ranked #7 on
Long-tail Learning
on VOC-MLT
Accelerated Proximal Gradient Methods for Nonconvex Programming
However, it is still unknown whether the usual APG can ensure the convergence to a critical point in nonconvex programming.
Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning
State of the art approaches for Semi-Supervised Learning (SSL) usually follow a two-stage framework -- constructing an affinity matrix from the data and then propagating the partial labels on this affinity matrix to infer those unknown labels.
Relaxed Majorization-Minimization for Non-smooth and Non-convex Optimization
We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods.
Convex Sparse Spectral Clustering: Single-view to Multi-view
Spectral Clustering (SC) is one of the most widely used methods for data clustering.
Fast Proximal Linearized Alternating Direction Method of Multiplier with Parallel Splitting
The Augmented Lagragian Method (ALM) and Alternating Direction Method of Multiplier (ADMM) have been powerful optimization methods for general convex programming subject to linear constraint.
Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm
The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal processing.
Optimized Projections for Compressed Sensing via Direct Mutual Coherence Minimization
To the best of our knowledge, this is the first work which directly minimizes the mutual coherence of the projected dictionary with a convergence guarantee.
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis
As an application, we also find that the solutions to extended robust Low-Rank Representation and to our extended robust MC are mutually expressible, so both our theory and algorithm can be applied to the subspace clustering problem with missing values under certain conditions.
Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion
Tag-based image retrieval (TBIR) has drawn much attention in recent years due to the explosive amount of digital images and crowdsourcing tags.
Subspace Clustering by Mixture of Gaussian Regression
Therefore, we propose Mixture of Gaussian Regression (MoG Regression) for subspace clustering by modeling noise as a Mixture of Gaussians (MoG).
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm
In the line search step, R3MC approximates the minimum point on the searching curve by minimizing on the line tangent to the curve.
Correlation Adaptive Subspace Segmentation by Trace Lasso
In this work, we argue that both sparsity and the grouping effect are important for subspace segmentation.
Correntropy Induced L2 Graph for Robust Subspace Clustering
In this paper, we study the robust subspace clustering problem, which aims to cluster the given possibly noisy data points into their underlying subspaces.
Generalized Singular Value Thresholding
This work studies the Generalized Singular Value Thresholding (GSVT) operator ${\text{Prox}}_{g}^{{\sigma}}(\cdot)$, \begin{equation*} {\text{Prox}}_{g}^{{\sigma}}(B)=\arg\min\limits_{X}\sum_{i=1}^{m}g(\sigma_{i}(X)) + \frac{1}{2}||X-B||_{F}^{2}, \end{equation*} associated with a nonconvex function $g$ defined on the singular values of $X$.
Relations among Some Low Rank Subspace Recovery Models
More specifically, we discover that once a solution to one of the models is obtained, we can obtain the solutions to other models in closed-form formulations.
Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features
This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting.
Robust Estimation of 3D Human Poses from a Single Image
We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons.
Ranked #25 on
3D Human Pose Estimation
on HumanEva-I
Adaptive Partial Differential Equation Learning for Visual Saliency Detection
Then by optimizing a discrete submodular function constrained with this LESD and a uniform matroid, the saliency seeds (i. e., boundary conditions) can be learnt for this image, thus achieving an optimal PDE system to model the evolution of visual saliency.
Robust Subspace Segmentation with Block-diagonal Prior
Most current state-of-the-art subspace segmentation methods (such as SSC and LRR) resort to alternative structural priors (such as sparseness and low-rankness) to construct the affinity matrix.
Smooth Representation Clustering
Based on our analysis, we propose the SMooth Representation (SMR) model.
Generalized Nonconvex Nonsmooth Low-Rank Minimization
We observe that all the existing nonconvex penalty functions are concave and monotonically increasing on $[0,\infty)$.
Proximal Iteratively Reweighted Algorithm with Multiple Splitting for Nonconvex Sparsity Optimization
This paper proposes the Proximal Iteratively REweighted (PIRE) algorithm for solving a general problem, which involves a large body of nonconvex sparse and structured sparse related problems.
Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization
Our convergence proof of IRLS is more general than previous one which depends on the special properties of the Schatten-$p$ norm and $\ell_{2, q}$-norm.
Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning
However, the traditional alternating direction method (ADM) and its linearized version (LADM, obtained by linearizing the quadratic penalty term) are for the two-block case and cannot be naively generalized to solve the multi-block case.
A Counterexample for the Validity of Using Nuclear Norm as a Convex Surrogate of Rank
For several rank minimization problems, such a replacement has been theoretically proven to be valid, i. e., the solution to nuclear norm minimization problem is also the solution to rank minimization problem.
Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation
It suffers from $O(n^3)$ computation complexity due to the matrix-matrix multiplications and matrix inversions, even if partial SVD is used.
Optimization and Control
Solving Principal Component Pursuit in Linear Time via $l_1$ Filtering
In this paper, we propose a novel algorithm, called $l_1$ filtering, for \emph{exactly} solving PCP with an $O(r^2(m+n))$ complexity, where $m\times n$ is the size of data matrix and $r$ is the rank of the matrix to recover, which is supposed to be much smaller than $m$ and $n$.
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms
Recent years have witnessed the popularity of using rank minimization as a regularizer for various signal processing and machine learning problems.
Robust Recovery of Subspace Structures by Low-Rank Representation
In this work we address the subspace recovery problem.
The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices
This paper proposes scalable and fast algorithms for solving the Robust PCA problem, namely recovering a low-rank matrix with an unknown fraction of its entries being arbitrarily corrupted.
Optimization and Control Numerical Analysis Systems and Control
Optimizing Multi-Class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification
The method of common spatio-spectral patterns (CSSPs) is an extension of common spatial patterns (CSPs) by utilizing the technique of delay embedding to alleviate the adverse effects of noises and artifacts on the electroencephalogram (EEG) classification.
