Channel Pruning Guided by Classification Loss and Feature Importance

In this work, we propose a new layer-by-layer channel pruning method called Channel Pruning guided by classification Loss and feature Importance (CPLI). In contrast to the existing layer-by-layer channel pruning approaches that only consider how to reconstruct the features from the next layer, our approach additionally take the classification loss into account in the channel pruning process. We also observe that some reconstructed features will be removed at the next pruning stage. So it is unnecessary to reconstruct these features. To this end, we propose a new strategy to suppress the influence of unimportant features (i.e., the features will be removed at the next pruning stage). Our comprehensive experiments on three benchmark datasets, i.e., CIFAR-10, ImageNet, and UCF-101, demonstrate the effectiveness of our CPLI method.Comment: AAAI202

Influence of Melatonin on Cerebrovascular Proinflammatory Mediators Expression and Oxidative Stress Following Subarachnoid Hemorrhage in Rabbits

The aim of this study is to analyze whether melatonin administration influenced the nuclear factor-kappa B (NF-κB) activity, proinflammatory cytokines expression, and oxidative response in the basilar artery after SAH. A total of 48 rabbits were randomly divided into four groups: control group, SAH group, SAH + vehicle group, and SAH + melatonin group. All SAH animals were subjected to injection of autologous blood into cisterna magna twice on day 0 and day 2. The melatonin was administered intraperitoneally at a dose of 5 mg/kg/12 h simultaneously with SAH from day 0 to day 5. The basilar arteries were extracted on day 5 after SAH. As a result, we found that vascular inflammation and oxidative stress were induced in all SAH animals. In animals given melatonin, basilar arterial NF-κB and pro-inflammatory cytokines were decreased in comparison to vehicle-treated animals. Measures of oxidative stress also showed significant downregulation after melatonin treatment. Furthermore, administration of melatonin prevented vasospasm on day 5 following SAH. In conclusion, post-SAH melatonin administration may attenuate inflammatory response and oxidative stress in the spasmodic artery, and this may be one mechanism involved in the therapeutic effect of melatonin on the subsequent vasospasm after SAH

Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types. All our models and datasets in this study are open sourced and can be downloaded from https://mekhub.cn/as/fault_diagnosis_with_few-shot_learning/

CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training

Pre-training across 3D vision and language remains under development because of limited training data. Recent works attempt to transfer vision-language pre-training models to 3D vision. PointCLIP converts point cloud data to multi-view depth maps, adopting CLIP for shape classification. However, its performance is restricted by the domain gap between rendered depth maps and images, as well as the diversity of depth distributions. To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification. We introduce a new depth rendering setting that forms a better visual effect, and then render 52,460 pairs of images and depth maps from ShapeNet for pre-training. The pre-training scheme of CLIP2Point combines cross-modality learning to enforce the depth features for capturing expressive visual and textual features and intra-modality learning to enhance the invariance of depth aggregation. Additionally, we propose a novel Dual-Path Adapter (DPA) module, i.e., a dual-path structure with simplified adapters for few-shot learning. The dual-path structure allows the joint use of CLIP and CLIP2Point, and the simplified adapter can well fit few-shot tasks without post-search. Experimental results show that CLIP2Point is effective in transferring CLIP knowledge to 3D vision. Our CLIP2Point outperforms PointCLIP and other self-supervised 3D networks, achieving state-of-the-art results on zero-shot and few-shot classification