Yu Xue

Yu Xue, Jiafeng Qin

Differentiable neural architecture search (DARTS), as a gradient-guided search method, greatly reduces the cost of computation and speeds up the search. In DARTS, the architecture parameters are introduced to the candidate operations, but the parameters of some weight-equipped operations may not be trained well in the initial stage, which causes unfair competition between candidate operations. The weight-free operations appear in large numbers which results in the phenomenon of performance crash. Besides, a lot of memory will be occupied during training supernet which causes the memory utilization to be low. In this paper, a partial channel connection based on channel attention for differentiable neural architecture search (ADARTS) is proposed. Some channels with higher weights are selected through the attention mechanism and sent into the operation space while the other channels are directly contacted with the processed channels. Selecting a few channels with higher attention weights can better transmit important feature information into the search space and greatly improve search efficiency and memory utilization. The instability of network structure caused by random selection can also be avoided. The experimental results show that ADARTS achieved 2.46% and 17.06% classification error rates on CIFAR-10 and CIFAR-100, respectively. ADARTS can effectively solve the problem that too many skip connections appear in the search process and obtain network structures with better performance.

Haoang Lu, Yuanqi Su, Xiaoning Zhang et al.

This paper introduces VisHall3D, a novel two-stage framework for monocular semantic scene completion that aims to address the issues of feature entanglement and geometric inconsistency prevalent in existing methods. VisHall3D decomposes the scene completion task into two stages: reconstructing the visible regions (vision) and inferring the invisible regions (hallucination). In the first stage, VisFrontierNet, a visibility-aware projection module, is introduced to accurately trace the visual frontier while preserving fine-grained details. In the second stage, OcclusionMAE, a hallucination network, is employed to generate plausible geometries for the invisible regions using a noise injection mechanism. By decoupling scene completion into these two distinct stages, VisHall3D effectively mitigates feature entanglement and geometric inconsistency, leading to significantly improved reconstruction quality. The effectiveness of VisHall3D is validated through extensive experiments on two challenging benchmarks: SemanticKITTI and SSCBench-KITTI-360. VisHall3D achieves state-of-the-art performance, outperforming previous methods by a significant margin and paves the way for more accurate and reliable scene understanding in autonomous driving and other applications.

Yu Xue, Ziming Yuan, Ferrante Neri

The so-called "attention" is an efficient mechanism to improve the performance of convolutional neural networks. It uses contextual information to recalibrate the input to strengthen the propagation of informative features. However, the majority of the attention mechanisms only consider either local or global contextual information, which is singular to extract features. Moreover, many existing mechanisms directly use the contextual information to recalibrate the input, which unilaterally enhances the propagation of the informative features, but does not suppress the useless ones. This paper proposes a new attention mechanism module based on the correlation between local and global contextual information and we name this correlation as confidence. The novel attention mechanism extracts the local and global contextual information simultaneously, and calculates the confidence between them, then uses this confidence to recalibrate the input pixels. The extraction of local and global contextual information increases the diversity of features. The recalibration with confidence suppresses useless information while enhancing the informative one with fewer parameters. We use CIFAR-10 and CIFAR-100 in our experiments and explore the performance of our method's components by sufficient ablation studies. Finally, we compare our method with a various state-of-the-art convolutional neural networks and the results show that our method completely surpasses these models. We implement ConAM with the Python library, Pytorch, and the code and models will be publicly available.

Yu Xue, Ziming Yuan, Adam Slowik

With the development of automatic sleep stage classification (ASSC) techniques, many classical methods such as k-means, decision tree, and SVM have been used in automatic sleep stage classification. However, few methods explore deep learning on ASSC. Meanwhile, most deep learning methods require extensive expertise and suffer from a mass of handcrafted steps which are time-consuming especially when dealing with multi-classification tasks. In this paper, we propose an efficient five-sleep-stage classification method using convolutional neural networks (CNNs) with a novel data processing trick and we design neural architecture search (NAS) technique based on genetic algorithm (GA), NAS-G, to search for the best CNN architecture. Firstly, we attach each kernel with an adaptive coefficient to enhance the signal processing of the inputs. This can enhance the propagation of informative features and suppress the propagation of useless features in the early stage of the network. Then, we make full use of GA's heuristic search and the advantage of no need for the gradient to search for the best architecture of CNN. This can achieve a CNN with better performance than a handcrafted one in a large search space at the minimum cost. We verify the convergence of our data processing trick and compare the performance of traditional CNNs before and after using our trick. Meanwhile, we compare the performance between the CNN generated through NAS-G and the traditional CNNs with our trick. The experiments demonstrate that the convergence of CNNs with data processing trick is faster than without data processing trick and the CNN with data processing trick generated by NAS-G outperforms the handcrafted counterparts that use the data processing trick too.

Yu Xue, Yihang Tang, Xin Xu et al.

Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a+ bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One of the main issues in real-world applications is missing data. Databases with missing data are likely to be unreliable. Thus, FS performed on a data set missing some data is also unreliable. In order to directly control this issue plaguing the field, we propose in this study a novel modelling of FS: we include reliability as the third objective of the problem. In order to address the modified problem, we propose the application of the non-dominated sorting genetic algorithm-III (NSGA-III). We selected six incomplete data sets from the University of California Irvine (UCI) machine learning repository. We used the mean imputation method to deal with the missing data. In the experiments, k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature subsets. Experimental results show that the proposed three-objective model coupled with NSGA-III efficiently addresses the FS problem for the six data sets included in this study.