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Found 61 papers, 14 papers with code
Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging
Hence the conventional Gaussian likelihood model is a suboptimal choice for the real-world source imaging task.
RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation
This approach enables each enhancement to focus on different GSFs, thereby achieving diverse feature representation in the enhanced structure.
Relation DETR: Exploring Explicit Position Relation Prior for Object Detection
The experimental results on the dataset illustrate that the proposed explicit position relation achieves a clear improvement of 1. 3% AP, highlighting its potential towards universal object detection.
Ranked #1 on
Object Detection
on SA-Det-100k
Discovering Common Information in Multi-view Data
We introduce an innovative and mathematically rigorous definition for computing common information from multi-view data, drawing inspiration from G\'acs-K\"orner common information in information theory.
Jacobian Regularizer-based Neural Granger Causality
With the advancement of neural networks, diverse methods for neural Granger causality have emerged, which demonstrate proficiency in handling complex data, and nonlinear relationships.
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications
Divergence measures play a central role and become increasingly essential in deep learning, yet efficient measures for multiple (more than two) distributions are rarely explored.
Soft Prompt Generation for Domain Generalization
During the inference phase, the generator of the generative model is employed to obtain instance-specific soft prompts for the unseen target domain.
Ranked #3 on
Domain Generalization
on VLCS
Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective
Pretrained vision-language models (VLMs) like CLIP have shown impressive generalization performance across various downstream tasks, yet they remain vulnerable to adversarial attacks.
Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings
Sparse Bayesian learning has promoted many effective frameworks for brain activity decoding, especially for the reconstruction of muscle activity.
Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consensus graph to for clustering.
Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
DETR-like methods have significantly increased detection performance in an end-to-end manner.
Ranked #3 on
Object Detection
on COCO 2017 val
NeuralDiffuser: Controllable fMRI Reconstruction with Primary Visual Feature Guided Diffusion
Reconstructing visual stimuli from functional Magnetic Resonance Imaging (fMRI) based on Latent Diffusion Models (LDM) provides a fine-grained retrieval of the brain.
Automatic Robotic Development through Collaborative Framework by Large Language Models
Analysts, programmers, and testers form a cohesive team overseeing strategy, code, and parameter adjustments .
Nonlinear subspace clustering by functional link neural networks
Additionally, we introduce a convex combination subspace clustering scheme, which combining a linear subspace clustering method with the functional link neural network subspace clustering approach.
Enhanced Latent Multi-view Subspace Clustering
Specifically, we stack the data matrices from various views into the block-diagonal locations of the augmented matrix to exploit the complementary information.
Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
Therefore, in this paper, we first experimentally demonstrate that the unsupervised-trained VLMs can significantly reduce the distribution discrepancy between source and target domains, thereby improving the performance of UDA.
Ranked #3 on
Unsupervised Domain Adaptation
on Office-31
Improving Cross-domain Few-shot Classification with Multilayer Perceptron
Multilayer perceptron (MLP) has shown its capability to learn transferable representations in various downstream tasks, such as unsupervised image classification and supervised concept generalization.
Ranked #3 on
Cross-Domain Few-Shot
on ChestX
Learning Discriminative Features for Crowd Counting
Crowd counting models in highly congested areas confront two main challenges: weak localization ability and difficulty in differentiating between foreground and background, leading to inaccurate estimations.
Head-Tail Cooperative Learning Network for Unbiased Scene Graph Generation
We also propose a self-supervised learning approach to enhance the prediction ability of the tail-prefer feature representation branch by constraining tail-prefer predicate features.
Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map
To improve the robustness to annotation displacement, we design an effective transport cost function based on GGD.
On the Adversarial Robustness of Generative Autoencoders in the Latent Space
The generative autoencoders, such as the variational autoencoders or the adversarial autoencoders, have achieved great success in lots of real-world applications, including image generation, and signal communication.
Gate Recurrent Unit Network based on Hilbert-Schmidt Independence Criterion for State-of-Health Estimation
State-of-health (SOH) estimation is a key step in ensuring the safe and reliable operation of batteries.
Adaptive sparseness for correntropy-based robust regression via automatic relevance determination
In the present study, regarding the maximum correntropy criterion (MCC) based robust regression algorithm, we investigate to integrate the MCC method with the automatic relevance determination (ARD) technique in a Bayesian framework, so that MCC-based robust regression could be implemented with adaptive sparseness.
CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis
There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-network based psychiatric diagnosis, which, in turn, also motivates an urgent need for psychiatrists to fully understand the decision behavior of the used GNNs.
Study of General Robust Subband Adaptive Filtering
In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty.
Correntropy-Based Logistic Regression with Automatic Relevance Determination for Robust Sparse Brain Activity Decoding
To this end, we introduce the correntropy learning framework into the automatic relevance determination based sparse classification model, proposing a new correntropy-based robust sparse logistic regression algorithm.
Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation
Thus, CNN could locate and estimate crowds accurately in low-density regions, while it is hard to properly perceive the densities in high-density regions.
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck
Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus.
Multi-view Information Bottleneck Without Variational Approximation
By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks.
Active noise control techniques for nonlinear systems
Most of the literature focuses on the development of the linear active noise control (ANC) techniques.
A survey on active noise control techniques -- Part I: Linear systems
Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems.
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals
The aim of this study is to propose a new robust implementation for PLSR.
Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting
More specifically, we scan the whole input images and its priority maps in the form of column vector to obtain a relevance matrix estimating their similarity.
Error Loss Networks
A novel model called error loss network (ELN) is proposed to build an error loss function for supervised learning.
Robust Motion Averaging under Maximum Correntropy Criterion
Recently, the motion averaging method has been introduced as an effective means to solve the multi-view registration problem.
Broad Learning System Based on Maximum Correntropy Criterion
As an effective and efficient discriminative learning method, Broad Learning System (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems.
Asymmetric Correntropy for Robust Adaptive Filtering
Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables.
Restricted Minimum Error Entropy Criterion for Robust Classification
The minimum error entropy (MEE) criterion has been verified as a powerful approach for non-Gaussian signal processing and robust machine learning.
An encoding framework with brain inner state for natural image identification
The framework comprises two parts: forward encoding model that deals with visual stimuli and inner state model that captures influence from intrinsic connections in the brain.
Multi-Kernel Correntropy for Robust Learning
In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths.
Maximum Correntropy Criterion with Variable Center
The kernel function in correntropy is usually restricted to the Gaussian function with center located at zero.
Consistency-aware Shading Orders Selective Fusion for Intrinsic Image Decomposition
We propose a Consistency-aware Selective Fusion (CSF) to integrate the pairwise orders into a globally consistent order.
Prognostics Estimations with Dynamic States
The health state assessment and remaining useful life (RUL) estimation play very important roles in prognostics and health management (PHM), owing to their abilities to reduce the maintenance and improve the safety of machines or equipment.
Augmented Space Linear Model
In this paper, we propose a linear model called Augmented Space Linear Model (ASLM), which uses the full joint space of input and desired signal as the projection space and approaches the performance of nonlinear models.
A Novel Brain Decoding Method: a Correlation Network Framework for Revealing Brain Connections
Analyzing the correlations of collected data from human brain activities and representing activity patterns are two problems in brain decoding based on functional magnetic resonance imaging (fMRI) signals.
Bias-Compensated Normalized Maximum Correntropy Criterion Algorithm for System Identification with Noisy Input
This paper proposed a bias-compensated normalized maximum correntropy criterion (BCNMCC) algorithm charactered by its low steady-state misalignment for system identification with noisy input in an impulsive output noise environment.
Quantized Minimum Error Entropy Criterion
Comparing with traditional learning criteria, such as mean square error (MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and non-Gaussian signal processing and machine learning.
Associations among Image Assessments as Cost Functions in Linear Decomposition: MSE, SSIM, and Correlation Coefficient
Moreover, for a target vector, the ratio of the corresponding affine parameters in the MSE-based linear decomposition scheme and the SSIM-based scheme is a constant, which is just the value of PCC between the target vector and its estimated vector.
Robustness of Maximum Correntropy Estimation Against Large Outliers
The maximum correntropy criterion (MCC) has recently been successfully applied in robust regression, classification and adaptive filtering, where the correntropy is maximized instead of minimizing the well-known mean square error (MSE) to improve the robustness with respect to outliers (or impulsive noises).
Brain Inspired Cognitive Model with Attention for Self-Driving Cars
Perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars.
Robust Learning with Kernel Mean p-Power Error Loss
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing.
Constrained Maximum Correntropy Adaptive Filtering
Most existing constrained adaptive filtering algorithms are developed under mean square error (MSE) criterion, which is an ideal optimality criterion under Gaussian noises.
Maximum Correntropy Unscented Filter
To improve the robustness of the UKF against impulsive noises, a new filter for nonlinear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF).
Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering
Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher-order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non-Gaussian signal processing and machine learning.
Similarity Learning With Spatial Constraints for Person Re-Identification
We therefore learn a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion.
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem.
Illumination Robust Color Naming via Label Propagation
In this paper we address the problem of inferring the color composition of the intrinsic reflectance of objects, where the shadows and highlights may change the observed color dramatically.
Maximum Correntropy Kalman Filter
Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption.
Diffusion Maximum Correntropy Criterion Algorithms for Robust Distributed Estimation
Robust diffusion adaptive estimation algorithms based on the maximum correntropy criterion (MCC), including adaptation to combination MCC and combination to adaptation MCC, are developed to deal with the distributed estimation over network in impulsive (long-tailed) noise environments.
Generalized Correntropy for Robust Adaptive Filtering
In this work, we propose a generalized correntropy that adopts the generalized Gaussian density (GGD) function as the kernel (not necessarily a Mercer kernel), and present some important properties.
Kernel Least Mean Square with Adaptive Kernel Size
Kernel adaptive filters (KAF) are a class of powerful nonlinear filters developed in Reproducing Kernel Hilbert Space (RKHS).
