Convolution Neural Networks (CNNs) have been used in various fields and are showing demonstrated excellent performance, especially in Single-Image Super Resolution (SISR). However, recently, CNN-based SISR has numerous parameters and computational costs for obtaining better performance. As one of the methods to make the network efficient, Knowledge Distillation (KD) which optimizes the performance trade-off by adding a loss term to the existing network architecture is currently being studied. KD for SISR is mainly proposed as a feature distillation (FD) to minimize L1-distance loss of feature maps between teacher and student networks, but it does not fully take into account the amount and importance of information that the student can accept. In this paper, we propose a feature-based adaptive contrastive distillation (FACD) method for efficiently training lightweight SISR networks. We show the limitations of the existing feature-distillation (FD) with L1-distance loss, and propose a feature-based contrastive loss that maximizes the mutual information between the feature maps of the teacher and student networks. The experimental results show that the proposed FACD improves not only the PSNR performance of the entire benchmark datasets and scales but also the subjective image quality compared to the conventional FD approach.

知识跟踪（KT）是一个研究领域，可以根据从智能辅导系统，学习管理系统和在线课程等教育应用程序中收集的先前绩效数据集来预测学生的未来表现。以前关于KT的研究仅集中在模型的解释性上，而其他研究则集中在增强性能上。考虑可解释性和提高性能的模型不足。此外，与现有模型相比，专注于绩效改进的模型尚未显示出压倒性的性能。在这项研究中，我们提出了Monacobert，该研究在大多数基准数据集上实现了最佳性能，并且具有明显的解释性。 Monacobert使用基于BERT的架构具有单调卷积多头注意，这反映了学生的遗忘行为并增加了模型的表示能力。我们还可以使用基于经典的测试理论（基于CTT）的嵌入策略来提高性能和解释性，该策略考虑了问题的难度。为了确定莫纳科伯特（Monacobert）为何达到最佳性能并定量解释结果，我们使用Grad-CAM，UMAP和各种可视化技术进行了消融研究和其他分析。分析结果表明，两个注意力组成部分相互补充，基于CTT的嵌入代表了有关全球和局部困难的信息。我们还证明了我们的模型代表概念之间的关系。

In this paper, we introduce an imagine network that can simulate itself through artificial association networks. Association, deduction, and memory networks are learned, and a network is created by combining the discriminator and reinforcement learning models. This model can learn various datasets or data samples generated in environments and generate new data samples.

我们介绍了记忆和记住任何数据的图形树内存网络。这个神经网络有两个回忆。一个由队列结构化的短期内存组成，可以解决类别不平衡问题和长期内存来存储对象的分发，引入存储和生成各种数据集的内容。

在本文中，我们介绍了图形树演绎网络，这是一种执行演绎推理的网络。为了产生新的关系和结果，需要高维思维，将各种公理和将结果放回另一个公理中，是必要的。例如，它会给两个命题：“苏格拉底是一个男人。”“所有人都是凡人。”两个命题可以用来推断出新的命题，“因此苏格拉底是凡人。”。为了评估，我们使用了Mnist DataSet，手写数值图像数据集，将其应用于组理论并显示执行演绎学习的结果。

在深度学习领域，已经开发了各种架构。然而，由于固定层结构，大多数研究限于特定的任务或数据集。本文不将信息提供作为网络模型的结构，而是作为称为关联树（AT）的数据结构。我们提出了两个人工协会网络（AAN），旨在通过分析人类神经网络的结构来解决现有网络的问题。定义单个图中的路径的起始点和结束点是困难的，并且树不能表达兄弟节点之间的关系。相反，AT可以表达叶子和根节点作为路径的起始点和兄弟节点之间的关系。根据树的结构而不是使用固定序列层，而不是使用固定序列图层为每个数据创建AT，并培训AAN。 AAN是数据驱动的学习，其中卷曲的数量根据树的深度而变化。此外，AAN可以通过递归学习同时学习各种类型的数据集。深度 - 第一卷积（DFC）将叶节点的交互结果以自下而上的方法对根节点进行编码到根节点，深度第一解码（DFD）将交互结果解码为自上而下的叶节点方法。我们进行了三个实验。第一个实验验证了是否可以通过组合AAN和特征提取网络来处理它。在第二，我们将网络的性能与单独学习的图像，声音和树，图形结构数据集进行了比较，通过连接这些网络同时学习的性能。在第三，我们验证了AAN的输出是否可以嵌入AT中的所有数据。因此，AATS学到了没有显着性能下降的情况。

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.