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Found 18 papers, 6 papers with code
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs.
Boosting Adversarial Transferability by Achieving Flat Local Maxima
Extensive experimental results on the ImageNet-compatible dataset show that the proposed method can generate adversarial examples at flat local regions, and significantly improve the adversarial transferability on either normally trained models or adversarially trained models than the state-of-the-art attacks.
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
Spherical videos, also known as \ang{360} (panorama) videos, can be viewed with various virtual reality devices such as computers and head-mounted displays.
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning
In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD).
signADAM: Learning Confidences for Deep Neural Networks
In this paper, we propose a new first-order gradient-based algorithm to train deep neural networks.
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.
Polarimetric Hierarchical Semantic Model and Scattering Mechanism Based PolSAR Image Classification
For polarimetric SAR (PolSAR) image classification, it is a challenge to classify the aggregated terrain types, such as the urban area, into semantic homogenous regions due to sharp bright-dark variations in intensity.
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation
This paper proposes a novel cascaded U-Net for brain tumor segmentation.
Efficient Relaxed Gradient Support Pursuit for Sparsity Constrained Non-convex Optimization
Large-scale non-convex sparsity-constrained problems have recently gained extensive attention.
Video Super Resolution Based on Deep Learning: A Comprehensive Survey
To the best of our knowledge, this work is the first systematic review on VSR tasks, and it is expected to make a contribution to the development of recent studies in this area and potentially deepen our understanding to the VSR techniques based on deep learning.
Boosting Gradient for White-Box Adversarial Attacks
Existing white-box attack algorithms can generate powerful adversarial examples.
Differentially Private ADMM Algorithms for Machine Learning
For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with performance guarantee of $(\epsilon,\delta)$-differential privacy ($(\epsilon,\delta)$-DP).
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling
In this paper, we propose a novel deep neural network with Dual Subnet and Multi-stage Communicated Upsampling (DSMC) for super-resolution of videos with large motion.
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning
Unlike existing FL methods, our IGFL can be applied to both client and server optimization.
Learned Interpretable Residual Extragradient ISTA for Sparse Coding
Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions.
Constrained Optimization Involving Nonconvex $\ell_p$ Norms: Optimality Conditions, Algorithm and Convergence
We also derive the sequential optimality conditions for both problems and study the conditions under which these conditions imply the first-order necessary conditions.
Accelerated Variance Reduced Stochastic Extragradient Method for Sparse Machine Learning Problems
Moreover, our theoretical analysis shows that AVR-SExtraGD enjoys the best-known convergence rates and oracle complexities of stochastic first-order algorithms such as Katyusha for both strongly convex and non-strongly convex problems.
Efficient High-Dimensional Data Representation Learning via Semi-Stochastic Block Coordinate Descent Methods
To address this issue, we propose a novel hard thresholding algorithm, called Semi-stochastic Block Coordinate Descent Hard Thresholding Pursuit (SBCD-HTP).
