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Found 16 papers, 9 papers with code
Momentum Batch Normalization for Deep Learning with Small Batch Size
To make a deeper understanding of BN, in this work we prove that BN actually introduces a certain level of noise into the sample mean and variance during the training process, while the noise level depends only on the batch size.
SGD with Partial Hessian for Deep Neural Networks Optimization
Compared with first-order optimizers, it adopts a certain amount of information from the Hessian matrix to assist optimization, while compared with the existing second-order optimizers, it keeps the good generalization performance of first-order optimizers.
Improving the Stability of Diffusion Models for Content Consistent Super-Resolution
To improve the stability of diffusion prior-based SR, we propose to employ the diffusion models to refine image structures, while employing the generative adversarial training to enhance image fine details.
Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective
High perceptual quality and low distortion degree are two important goals in image restoration tasks such as super-resolution (SR).
Spatial-Frequency Attention for Image Denoising
In this paper, we propose the spatial-frequency attention network (SFANet) to enhance the network's ability in exploiting long-range dependency.
A General Regret Bound of Preconditioned Gradient Method for DNN Training
Though the full-matrix preconditioned gradient methods theoretically have a lower regret bound, they are impractical for use to train DNNs because of the high complexity.
Unfolded Deep Kernel Estimation for Blind Image Super-resolution
Nonetheless, the existing deep unfolding methods cannot explicitly solve the data term of the unfolding objective function, limiting their capability in blur kernel estimation.
Deep Convolutional Dictionary Learning for Image Denoising
Inspired by the great success of deep neural networks (DNNs), many unfolding methods have been proposed to integrate traditional image modeling techniques, such as dictionary learning (DicL) and sparse coding, into DNNs for image restoration.
Deep Variational Network Toward Blind Image Restoration
In this proposed model, a pixel-wise non-i. i. d.
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
It has been shown that using the first and second order statistics (e. g., mean and variance) to perform Z-score standardization on network activations or weight vectors, such as batch normalization (BN) and weight standardization (WS), can improve the training performance.
Variational Denoising Network: Toward Blind Noise Modeling and Removal
On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression.
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Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model
Most of the existing learning-based single image superresolution (SISR) methods are trained and evaluated on simulated datasets, where the low-resolution (LR) images are generated by applying a simple and uniform degradation (i. e., bicubic downsampling) to their high-resolution (HR) counterparts.
Model Inconsistent but Correlated Noise: Multi-view Subspace Learning with Regularized Mixture of Gaussians
Different from single view case, MSL should take both common and specific knowledge among different views into consideration.
Sharp Attention Network via Adaptive Sampling for Person Re-identification
In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks (CNNs) for person re-identification (re-ID) problem.
Robust Online Matrix Factorization for Dynamic Background Subtraction
We propose an effective online background subtraction method, which can be robustly applied to practical videos that have variations in both foreground and background.
Group MAD Competition - A New Methodology to Compare Objective Image Quality Models
We first build a database that contains 4, 744 source natural images, together with 94, 880 distorted images created from them.
