Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://github.com/rain305f/OSP.Comment: Accpected by CVPR 202

Simvastatin reduces atherogenesis and promotes the expression of hepatic genes associated with reverse cholesterol transport in apoE-knockout mice fed high-fat diet

<p>Abstract</p> <p>Background</p> <p>Statins are first-line pharmacotherapeutic agents for hypercholesterolemia treatment in humans. However the effects of statins on atherosclerosis in mouse models are very paradoxical. In this work, we wanted to evaluate the effects of simvastatin on serum cholesterol, atherogenesis, and the expression of several factors playing important roles in reverse cholesterol transport (RCT) in apoE-/- mice fed a high-fat diet.</p> <p>Results</p> <p>The atherosclerotic lesion formation displayed by oil red O staining positive area was reduced significantly by 35% or 47% in either aortic root section or aortic arch en face in simvastatin administrated apoE-/- mice compared to the control. Plasma analysis by enzymatic method or ELISA showed that high-density lipoprotein-cholesterol (HDL-C) and apolipoprotein A-I (apoA-I) contents were remarkably increased by treatment with simvastatin. And plasma lecithin-cholesterol acyltransferase (LCAT) activity was markedly increased by simvastatin treatment. Real-time PCR detection disclosed that the expression of several transporters involved in reverse cholesterol transport, including macrophage scavenger receptor class B type I, hepatic ATP-binding cassette (ABC) transporters ABCG5, and ABCB4 were induced by simvastatin treatment, the expression of hepatic ABCA1 and apoA-I, which play roles in the maturation of HDL-C, were also elevated in simvastatin treated groups.</p> <p>Conclusions</p> <p>We demonstrated the anti-atherogenesis effects of simvastatin in apoE-/- mice fed a high-fat diet. We confirmed here for the first time simvastatin increased the expression of hepatic ABCB4 and ABCG5, which involved in secretion of cholesterol and bile acids into the bile, besides upregulated ABCA1 and apoA-I. The elevated HDL-C level, increased LCAT activity and the stimulation of several transporters involved in RCT may all contribute to the anti-atherosclerotic effect of simvastatin.</p

L2L_2BN: Enhancing Batch Normalization by Equalizing the L2L_2 Norms of Features

In this paper, we show that the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features. To address this issue, we propose an intuitive but effective method to equalize the $l_2$ norms of sample features. Concretely, we $l_2$-normalize each sample feature before feeding them into batch normalization, and therefore the features are of the same magnitude. Since the proposed method combines the $l_2$ normalization and batch normalization, we name our method $L_2$BN. The $L_2$BN can strengthen the compactness of intra-class features and enlarge the discrepancy of inter-class features. The $L_2$BN is easy to implement and can exert its effect without any additional parameters or hyper-parameters. Therefore, it can be used as a basic normalization method for neural networks. We evaluate the effectiveness of $L_2$BN through extensive experiments with various models on image classification and acoustic scene classification tasks. The results demonstrate that the $L_2$BN can boost the generalization ability of various neural network models and achieve considerable performance improvements

Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods.Comment: Accepted to NeurIPS 202

Contrasting off-line segmentation decisions with on-line word segmentation during reading

In two experiments, we investigated the correspondences between off-line word segmentation and on-line segmentation processing during Chinese reading. In Experiment 1, participants were asked to read sentences which contained critical four-character strings, and then they were required to segment the same sentences into words in a later off-line word segmentation task. For each item, participants were split into 1-word segmenters (who segmented four-character strings as a single word) and 2-word segmenters (who segmented four-character strings as 2 two-character words). Thus, we split participants into two groups (1-word segmenters and 2-word segmenters) according to their off-line segmentation bias. The data analysis showed no reliable group effect on all the measures. In order to avoid the heterogeneity of participants and stimuli in Experiment 1, two groups of participants (1-word segmenters and 2-word segmenters) and three types of critical four-character string (1-word strings, ambiguous strings and 2-word strings) were identified in a norming study in Experiment 2. Participants were required to read sentences containing these critical strings. There was no reliable group effect in Experiment 2, as was the case in Experiment 1. However, in Experiment 2, participants spent less time and made fewer fixations on 1-word strings compared to ambiguous and 2-word strings. These results indicate that the off-line word segmentation preferences do not necessarily reflect on-line word segmentation processing during Chinese reading, and that Chinese readers exhibit flexibility such that word, or multiple constituent, segmentation commitments are made on-line

MF-Net: multi-scale feature extraction-integration network for unsupervised deformable registration

Deformable registration plays a fundamental and crucial role in scenarios such as surgical navigation and image-assisted analysis. While deformable registration methods based on unsupervised learning have shown remarkable success in predicting displacement fields with high accuracy, many existing registration networks are limited by the lack of multi-scale analysis, restricting comprehensive utilization of global and local features in the images. To address this limitation, we propose a novel registration network called multi-scale feature extraction-integration network (MF-Net). First, we propose a multiscale analysis strategy that enables the model to capture global and local semantic information in the image, thus facilitating accurate texture and detail registration. Additionally, we introduce grouped gated inception block (GI-Block) as the basic unit of the feature extractor, enabling the feature extractor to selectively extract quantitative features from images at various resolutions. Comparative experiments demonstrate the superior accuracy of our approach over existing methods