图像合成中的评估指标起着测量生成模型的性能的关键作用。但是，大多数指标主要集中于图像保真度。现有的多样性指标是通过比较分布来得出的，因此它们无法量化每个生成图像的多样性或稀有程度。在这项工作中，我们提出了一个新的评估度量，称为“稀有分数”，以测量通过生成模型合成的每个图像的稀有性。我们首先表明经验观察表明，共同样品彼此接近，并且在特征空间最近的邻居距离处，稀有的样本彼此遥远。然后，我们使用我们的指标来证明可以有效比较不同生成模型产生稀有图像的程度。我们还提出了一种比较共享相同概念（例如Celeba-HQ和FFHQ）的数据集之间的稀有度的方法。最后，我们分析了在特征空间的不同设计中的指标的使用，以更好地了解特征空间和产生的稀疏图像之间的关系。代码将在网上公开用于研究社区。

在本文中，我们提出了一种三阶段培训方法，提高低资源语言的语音识别准确性。我们探索并提出了一种有效的技术组合，如传输学习，编码器冻结，使用文本到语音（TTS）和半监督学习（SSL）。为了提高低资源意大利ASR的准确性，我们可以分别利用训练有素的英语模型，未标记的文本语料库和未标记的音频语料库，分别分别使用传输学习，TTS增强和SSL。在第一阶段，我们使用从训练有素的英语模型的转移学习。这主要有助于学习来自资源丰富的语言的声学信息。该阶段通过基线减少约24％的相对字错误率（WER）。在第二阶段，我们通过TTS数据增强利用未标记的文本数据来将语言信息合并到模型中。我们还在此阶段探索冻结声学编码器。 TTS数据增强有助于我们进一步减少〜21％相对〜21％。最后，在第三阶段，我们通过使用来自未标记的音频数据的SSL来减少另一个4％的相对。总体而言，我们的双通话识别系统在第一次通过的单调散文注意力（Mocha）和第二次通过的全部关注，相对于基线，减少了〜42％的WER。

现在，通过复杂的神经网络模型（例如蒙版的神经语言模型（MNLM））学习了许多上下文化的单词表示形式，这些模型由巨大的神经网络结构组成，并经过训练以恢复蒙面文本。这样的表示表明在某些阅读理解（RC）任务中表现出超人的表现，这些任务在给出问题的上下文中提取了适当的答案。但是，由于许多模型参数，确定在MNLM中训练的详细知识是具有挑战性的。本文提供了有关MNLMS中包含的常识性知识的新见解和经验分析。首先，我们使用诊断测试来评估常识性知识是否在MNLMS中进行了适当的培训。我们观察到，在MNLMS中没有适当训练很多常识性知识，并且MNLMS并不经常准确地理解关系的语义含义。此外，我们发现基于MNLM的RC模型仍然容易受到需要常识知识的语义变化的影响。最后，我们发现了未经训练的知识的基本原因。我们进一步建议，利用外常识性知识存储库可以是一个有效的解决方案。我们说明了通过在受控实验中以外常识性知识存储库来丰富文本的经文，以克服基于MNLM的RC模型的局限性的可能性。

野外表达对于各种交互式计算域至关重要。特别是，“从合成数据学习”（LSD）是面部表达识别任务中的重要主题。在本文中，我们提出了一种基于多任务的面部表达识别方法，该方法由情感和外观学习分支组成，可以共享所有面部信息，并为第四个情感行为分析中引入的LSD挑战提供初步结果。-Wild（ABAW）比赛。我们的方法达到的平均F1得分为0.71。

尽管对生成对冲网络（GANS）的图像生成性能有重大改进，但仍然观察到具有低视觉保真度的代。随着GAN的广泛使用指标，更多地关注模型的整体性能，对个体代的质量或缺陷代的检测的评估是具有挑战性的。虽然最近的研究试图检测导致伪像和评估单个样本的特派团映射单元，但这些方法需要额外的资源，例如外部网络或许多训练数据来近似真实数据歧管。在这项工作中，我们提出了本地激活的概念，并设计了本地激活的度量，以检测没有额外监督的工件代。我们经验验证我们的方法可以从带有各种数据集的GAN检测和纠正工件代。最后，我们讨论了几何分析，以部分揭示所提出的概念和低视力忠诚之间的关系。

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.

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.