Cross-Domain Labeled LDA for Cross-Domain Text Classification

Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the feature distribution differences of the two domains. Most existing studies explicitly minimize such differences by an exact alignment mechanism (aligning features by one-to-one feature alignment, projection matrix etc.). Such exact alignment, however, will restrict models' learning ability and will further impair models' performance on classification tasks when the semantic distributions of different domains are very different. To address this problem, we propose a novel group alignment which aligns the semantics at group level. In addition, to help the model learn better semantic groups and semantics within these groups, we also propose a partial supervision for model's learning in source domain. To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and Reuters dataset, extensive quantitative (classification, perplexity etc.) and qualitative (topic detection) experiments are conducted to show the effectiveness of the proposed group alignment and partial supervision.Comment: ICDM 201

Sharpness-aware Quantization for Deep Neural Networks

Network quantization is an effective compression method to reduce the model size and computational cost. Despite the high compression ratio, training a low-precision model is difficult due to the discrete and non-differentiable nature of quantization, resulting in considerable performance degradation. Recently, Sharpness-Aware Minimization (SAM) has been proposed to improve the generalization performance of the models by simultaneously minimizing the loss value and the loss curvature. However, SAM can not be directly applied to quantized models due to the discretization process in network quantization. In this paper, we devise a Sharpness-Aware Quantization (SAQ) method to train quantized models, leading to better generalization performance. Moreover, since each layer contributes differently to the loss value and the loss sharpness of a network, we further devise an effective method that learns a configuration generator to automatically determine the bitwidth configurations of each layer, encouraging lower bits for flat regions and vice versa for sharp landscapes, while simultaneously promoting the flatness of minima to enable more aggressive quantization. Extensive experiments on CIFAR-100 and ImageNet show the superior performance of the proposed methods. For example, our quantized ResNet-18 with 53.7x Bit-Operation (BOP) reduction even outperforms the full-precision one by 0.7% in terms of the Top-1 accuracy. Code is available at https://github.com/zip-group/SAQ.Comment: Tech repor

Relation between axial length and ocular parameters

AIM: To investigatethe relation between axial length(AL), age and ocular parameters.<p>METHODS: A total of 360 subjects(360 eyes)with emmetropia or myopia were recruited. Refraction, center corneal thickness(CCT), AL, intraocular pressure(IOP)were measured by automatic-refractor, Pachymeter, A-mode ultrasound and non-contact tonometer, respectively. Corneal curvature(CC), anterior chamber depth(ACD)and white-to-white distance(WWD)were measured by Orbscan II. Three dimensional frequency domain coherent optical tomography(3D-OCT)was used to examine the retinal nerve fiber layer thickness(RNFLT). The Pearson correlation coefficient(<i>r</i>)and multiple regression analysis were performed to evaluate the relationship between AL, age and ocular parameters.<p>RESULTS: The average AL was 24.15±1.26mm. With elongation of the AL, spherical equivalent(SE)(<i>r</i>=-0.742,<i>P</i><0.01), CC(<i>r</i>=-0.395, <i>P</i><0.01)and RNFLT(<i>r</i>=-0.374, <i>P</i><0.01)all decreased, while the mean ACD(<i>r</i>=0.411, <i>P</i><0.01)increased. On the contrary, there was not statistical significan with CCT(<i>r</i>=0.099, <i>P</i>=0.060)and WWD(<i>r</i>=0.061, <i>P</i>=0.252). There was also a significant correlation between AL and age(<i>P</i>=0.001), SE(<i>P</i><0.001), ACD(<i>P</i><0.001), CC(<i>P</i><0.001)in Multiple linear regression analysis.<p>CONCLUSION: In longer eyes, there is a tendency toward myopia, a flatter cornea, a deeper ACD and a thinner RNFLT. Age is an influencing factor for the AL as well