Network Binarization via Contrastive Learning

Neural network binarization accelerates deep models by quantizing their weights and activations into 1-bit. However, there is still a huge performance gap between Binary Neural Networks (BNNs) and their full-precision (FP) counterparts. As the quantization error caused by weights binarization has been reduced in earlier works, the activations binarization becomes the major obstacle for further improvement of the accuracy. BNN characterises a unique and interesting structure, where the binary and latent FP activations exist in the same forward pass (i.e., $\text{Binarize}(\mathbf{a}_F) = \mathbf{a}_B$). To mitigate the information degradation caused by the binarization operation from FP to binary activations, we establish a novel contrastive learning framework while training BNNs through the lens of Mutual Information (MI) maximization. MI is introduced as the metric to measure the information shared between binary and FP activations, which assists binarization with contrastive learning. Specifically, the representation ability of the BNNs is greatly strengthened via pulling the positive pairs with binary and FP activations from the same input samples, as well as pushing negative pairs from different samples (the number of negative pairs can be exponentially large). This benefits the downstream tasks, not only classification but also segmentation and depth estimation, etc. The experimental results show that our method can be implemented as a pile-up module on existing state-of-the-art binarization methods and can remarkably improve the performance over them on CIFAR-10/100 and ImageNet, in addition to the great generalization ability on NYUD-v2.Comment: Accepted to ECCV 202

Lipschitz Continuity Retained Binary Neural Network

Relying on the premise that the performance of a binary neural network can be largely restored with eliminated quantization error between full-precision weight vectors and their corresponding binary vectors, existing works of network binarization frequently adopt the idea of model robustness to reach the aforementioned objective. However, robustness remains to be an ill-defined concept without solid theoretical support. In this work, we introduce the Lipschitz continuity, a well-defined functional property, as the rigorous criteria to define the model robustness for BNN. We then propose to retain the Lipschitz continuity as a regularization term to improve the model robustness. Particularly, while the popular Lipschitz-involved regularization methods often collapse in BNN due to its extreme sparsity, we design the Retention Matrices to approximate spectral norms of the targeted weight matrices, which can be deployed as the approximation for the Lipschitz constant of BNNs without the exact Lipschitz constant computation (NP-hard). Our experiments prove that our BNN-specific regularization method can effectively strengthen the robustness of BNN (testified on ImageNet-C), achieving state-of-the-art performance on CIFAR and ImageNet.Comment: Paper accepted to ECCV 202

Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance

The chest X-ray is often utilized for diagnosing common thoracic diseases. In recent years, many approaches have been proposed to handle the problem of automatic diagnosis based on chest X-rays. However, the scarcity of labeled data for related diseases still poses a huge challenge to an accurate diagnosis. In this paper, we focus on the thorax disease diagnostic problem and propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents and the model parameters can also be continuously updated as the data increases, like a person's learning process. Especially, 1) prior knowledge can be learned from the pre-trained model based on old data or other domains' similar data, which can effectively reduce the dependence on target domain data, and 2) the framework of reinforcement learning can make the diagnostic agent as exploratory as a human being and improve the accuracy of diagnosis through continuous exploration. The method can also effectively solve the model learning problem in the case of few-shot data and improve the generalization ability of the model. Finally, our approach's performance was demonstrated using the well-known NIH ChestX-ray 14 and CheXpert datasets, and we achieved competitive results. The source code can be found here: \url{https://github.com/NeaseZ/MARL}

4-[(E)-(2,3-Dichloro­benzyl­idene)amino]­phenol

In the title compound, C13H9Cl2NO, the dihedral angle between the benzene rings is 54.22 (10)°. In the crystal, mol­ecules are linked by O—H⋯N inter­molecular hydrogen bonds, forming a zigzag C(7) chain along the a axis

Advances in Polygonatum sibiricum polysaccharides: Extraction, purification, structure, biosynthesis, and bioactivity

Polygonatum sibiricum has been used as food and medicine for thousands of years, and P. sibiricum polysaccharides (PSPs) have become the hot research spot due to their various health-promoting functions. Numerous studies have shown that PSPs possess huge potential in the application of functional food and medicine fields. However, the research status and features of the preparation process, molecular structure, and bioactivities of PSPs are unclear. Therefore, this review makes a comprehensive summary and proposes new insights and guidelines for the extraction, purification, structural features, biosynthesis, and multiple bioactivities of PSPs. Notably, it is concluded that PSPs mainly contain several types of polysaccharides, including fructan, pectin, galactomannan, glucomannans, arabinogalactan, and galactan, and multiple bioactivates, including osteogenic activity, anti-obesity, anti-diabetes, anti-depression, antioxidant, antiglycation, and protective effect against neurotoxicity and gut microbiota regulating activity. This review contributes to the structure–function study and resource utilization of P. sibiricum and its polysaccharides in food fields