57 research outputs found
Multi-Task Pruning for Semantic Segmentation Networks
This paper focuses on channel pruning for semantic segmentation networks.
There are a large number of works to compress and accelerate deep neural
networks in the classification task (e.g., ResNet-50 on ImageNet), but they
cannot be straightforwardly applied to the semantic segmentation network that
involves an implicit multi-task learning problem. To boost the segmentation
performance, the backbone of semantic segmentation network is often pre-trained
on a large scale classification dataset (e.g., ImageNet), and then optimized on
the desired segmentation dataset. Hence to identify the redundancy in
segmentation networks, we present a multi-task channel pruning approach. The
importance of each convolution filter w.r.t the channel of an arbitrary layer
will be simultaneously determined by the classification and segmentation tasks.
In addition, we develop an alternative scheme for optimizing importance scores
of filters in the entire network. Experimental results on several benchmarks
illustrate the superiority of the proposed algorithm over the state-of-the-art
pruning methods. Notably, we can obtain an about FLOPs reduction on
DeepLabv3 with only an about mIoU drop on the PASCAL VOC 2012 dataset and
an about mIoU drop on Cityscapes dataset, respectively
Double Normalizing Flows: Flexible Bayesian Gaussian Process ODEs Learning
Recently, Gaussian processes have been utilized to model the vector field of
continuous dynamical systems. Bayesian inference for such models
\cite{hegde2022variational} has been extensively studied and has been applied
in tasks such as time series prediction, providing uncertain estimates.
However, previous Gaussian Process Ordinary Differential Equation (ODE) models
may underperform on datasets with non-Gaussian process priors, as their
constrained priors and mean-field posteriors may lack flexibility. To address
this limitation, we incorporate normalizing flows to reparameterize the vector
field of ODEs, resulting in a more flexible and expressive prior distribution.
Additionally, due to the analytically tractable probability density functions
of normalizing flows, we apply them to the posterior inference of GP ODEs,
generating a non-Gaussian posterior. Through these dual applications of
normalizing flows, our model improves accuracy and uncertainty estimates for
Bayesian Gaussian Process ODEs. The effectiveness of our approach is
demonstrated on simulated dynamical systems and real-world human motion data,
including tasks such as time series prediction and missing data recovery.
Experimental results indicate that our proposed method effectively captures
model uncertainty while improving accuracy
Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection
Distinguishing between computer-generated (CG) and natural photographic (PG)
images is of great importance to verify the authenticity and originality of
digital images. However, the recent cutting-edge generation methods enable high
qualities of synthesis in CG images, which makes this challenging task even
trickier. To address this issue, a joint learning strategy with deep texture
and high-frequency features for CG image detection is proposed. We first
formulate and deeply analyze the different acquisition processes of CG and PG
images. Based on the finding that multiple different modules in image
acquisition will lead to different sensitivity inconsistencies to the
convolutional neural network (CNN)-based rendering in images, we propose a deep
texture rendering module for texture difference enhancement and discriminative
texture representation. Specifically, the semantic segmentation map is
generated to guide the affine transformation operation, which is used to
recover the texture in different regions of the input image. Then, the
combination of the original image and the high-frequency components of the
original and rendered images are fed into a multi-branch neural network
equipped with attention mechanisms, which refines intermediate features and
facilitates trace exploration in spatial and channel dimensions respectively.
Extensive experiments on two public datasets and a newly constructed dataset
with more realistic and diverse images show that the proposed approach
outperforms existing methods in the field by a clear margin. Besides, results
also demonstrate the detection robustness and generalization ability of the
proposed approach to postprocessing operations and generative adversarial
network (GAN) generated images
Capacity-based Spatial Modulation Constellation and Pre-scaling Design
Spatial Modulation (SM) can utilize the index of the transmit antenna (TA) to
transmit additional information. In this paper, to improve the performance of
SM, a non-uniform constellation (NUC) and pre-scaling coefficients optimization
design scheme is proposed. The bit-interleaved coded modulation (BICM) capacity
calculation formula of SM system is firstly derived. The constellation and
pre-scaling coefficients are optimized by maximizing the BICM capacity without
channel state information (CSI) feedback. Optimization results are given for
the multiple-input-single-output (MISO) system with Rayleigh channel.
Simulation result shows the proposed scheme provides a meaningful performance
gain compared to conventional SM system without CSI feedback. The proposed
optimization design scheme can be a promising technology for future 6G to
achieve high-efficiency.Comment: 6 pages,conferenc
Association Between Premorbid Body Mass Index and Amyotrophic Lateral Sclerosis: Causal Inference Through Genetic Approaches
Purpose: Inverse association between premorbid body mass index (BMI) and amyotrophic lateral sclerosis (ALS) was implied in observational studies; however, whether this association is causal remains largely unknown.Materials and Methods: We first conducted a meta-analysis to investigate whether there exits an association between premorbid BMI and ALS. We then employed a two-sample Mendelian randomization approach to evaluate the causal relationship of genetically increased BMI with the risk of ALS. The Mendelian randomization analysis was implemented using summary statistics for independent instruments obtained from large-scale genome-wide association studies of BMI (up to ~770,000 individuals) and ALS (up to ~81,000 individuals). The causal effect of BMI on ALS was estimated using inverse-variance weighted methods and was further validated through extensive complementary and sensitivity analyses.Results: The meta-analysis showed that a unit increase of premorbid BMI can result in about 3.0% (95% CI 2.1–4.5%) risk reduction of ALS. Using 1,031 instruments that were strongly related to BMI, the causal effect of per one standard deviation increase of BMI was estimated to be 1.04 (95% CI 0.97–1.11, p = 0.275) in the European population. This null association between BMI and ALS also held in the East Asian population and was robust against various modeling assumptions and outlier biases. Additionally, the Egger-regression and MR-PRESSO ruled out the possibility of horizontal pleiotropic effects of instruments.Conclusion: Our results do not support the causal role of genetically increased or decreased BMI on the risk of ALS
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