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Found 12 papers, 5 papers with code
Accelerating Inference of Networks in the Frequency Domain
In particular, we propose a frequency inference chain that is dual to the network inference in the spatial domain.
Deep Clustering via Distribution Learning
Thus, in this work, we provide a theoretical analysis to guide the optimization of clustering via distribution learning.
Medical Image Denosing via Explainable AI Feature Preserving Loss
Denoising algorithms play a crucial role in medical image processing and analysis.
Frequency Regularization: Reducing Information Redundancy in Convolutional Neural Networks
Since high-frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization.
Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module
On this foundation, we present a novel Differentiable Arithmetic Distribution Module (DADM), which is designed to extract the intrinsic probability distributions from images.
Bridging Distribution Learning and Image Clustering in High-dimensional Space
Based on the experimental results, we believe distribution learning can exploit the potential of GMM in image clustering within high-dimensional space.
Is Deep Learning Network Necessary for Image Generation?
In this paper, we investigate the possibility of image generation without using a deep learning network, motivated by validating the assumption that images follow a high-dimensional distribution.
Learning Distributions via Monte-Carlo Marginalization
One strong evidence of the benefit of our method is that the distributions learned by the proposed approach can generate better images even based on a pre-trained VAE's decoder.
Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation
In this paper, we propose a method called Learning Temporal Distribution and Spatial Correlation (LTS) that has the potential to be a general solution for universal moving object segmentation.
Frequency Regularization: Restricting Information Redundancy of Convolutional Neural Networks
Since high frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization.
SPGNet: Spatial Projection Guided 3D Human Pose Estimation in Low Dimensional Space
We propose a method SPGNet for 3D human pose estimation that mixes multi-dimensional re-projection into supervised learning.
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3D Human Pose Estimation
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