我们提出了一种用于语义分割的新型无监督域适应方法，该方法将训练的模型概括为源图像和相应的地面真相标签到目标域。域自适应语义分割的关键是学习域，不变和判别特征，而无需目标地面真相标签。为此，我们提出了一个双向像素 - 型对比型学习框架，该框架可最大程度地减少同一对象类特征的类内变化，同时无论域，无论域如何，都可以最大程度地提高不同阶层的阶层变化。具体而言，我们的框架将像素级特征与目标和源图像中同一对象类的原型保持一致（即分别为正面对），将它们设置为不同的类别（即负对），并执行对齐和分离在源图像中具有像素级特征的另一个方向的过程，目标图像中的原型。跨域匹配鼓励域不变特征表示，而双向像素 - 型对应对应关系汇总了同一对象类的特征，提供了歧视性特征。为了建立对比度学习的训练对，我们建议使用非参数标签转移（即跨不同域的像素 - 型对应关系，就可以生成目标图像的动态伪标签。我们还提出了一种校准方法，以补偿训练过程中逐渐补偿原型的阶级域偏差。

虽然多步逆势培训被广泛流行作为对抗强烈的对抗攻击的有效防御方法，但其计算成本与标准培训相比，其计算成本是众所周知的。已经提出了几种单步侵权培训方法来减轻上述开销费用;但是，根据优化设置，它们的性能并不能充分可靠。为了克服这些限制，我们偏离了现有的基于输入空间的对抗性培训制度，并提出了一种单步潜在培训方法（SLAT），其利用潜在的代表梯度作为潜在的对抗扰动。我们证明，与所采用的潜伏扰动，恢复局部线性度并确保与现有的单步逆势训练方法相比，恢复局部线性度并确保可靠性的特征梯度的L1规范。因为潜伏的扰动基于可以在输入梯度计算过程中免费获得的潜在表示的梯度，所以所提出的方法与快速梯度标志方法相当成本。实验结果表明，尽管其结构简单，但优于最先进的加速的对抗训练方法。

对抗性学习策略在处理单源域适应（DA）问题时表现出显着的性能，并且最近已应用于多源DA（MDA）问题。虽然大多数现有的MDA策略依赖于多个域歧视员设置，但其对潜伏空间表示的影响已经不知识。在这里，我们采用了一种信息 - 理论方法来识别和解决MDA上多个域鉴别器的潜在不利影响：域歧视信息的解体，有限的计算可扩展性以及培训期间损失梯度的大方差。我们在信息正规化的背景下通过情况进行对抗性DA来检查上述问题。这还提供了使用单一和统一域鉴别器的理论正当理由。基于这个想法，我们实施了一种名为多源信息正规化适应网络（MIAN）的新型神经结构。大规模实验表明，尽管其结构简洁，可靠，可显着优于其他最先进的方法。

神经网络量化旨在将特定神经网络的高精度权重和激活转变为低精度的权重/激活，以减少存储器使用和计算，同时保留原始模型的性能。但是，紧凑设计的主链体系结构（例如Mobilenets）通常用于边缘设备部署的极端量化（1位重量/1位激活）会导致严重的性能变性。本文提出了一种新颖的量化感知训练（QAT）方法，即使通过重点关注各层之间的权重之间的重量间依赖性，也可以通过极端量化有效地减轻性能退化。为了最大程度地减少每个重量对其他重量的量化影响，我们通过训练一个依赖输入依赖性的相关矩阵和重要性向量来对每一层的权重进行正交转换，从而使每个权重都与其他权重分开。然后，我们根据权重量化的重要性来最大程度地减少原始权重/激活中信息丢失的重要性。我们进一步执行从底层到顶部的渐进层量化，因此每一层的量化都反映了先前层的权重和激活的量化分布。我们验证了我们的方法对各种基准数据集的有效性，可针对强神经量化基线，这表明它可以减轻ImageNet上的性能变性，并成功地保留了CIFAR-100上具有紧凑型骨干网络的完整精确模型性能。

在实用的联合学习方案中，参与的设备可能具有不同的位宽，用于按设计进行计算和内存存储。然而，尽管设备异构联合学习方案取得了进展，但硬件中位于位的比值的异质性大多被忽略了。我们介绍了一种务实的FL场景，在参与设备中具有位于刻度的异质性，被称为Bitwidth异质联邦学习（BHFL）。 BHFL提出了一个新的挑战，即具有不同位宽度的模型参数的聚合可能会导致严重的性能变性，尤其是对于高含宽模型。为了解决这个问题，我们提出了ProWD框架，该框架在中央服务器上具有可训练的权重去除剂，该框架逐渐将低位宽度的重量重建为更高的位宽度重量，最后将其重建为完整的重量。 PROWD进一步选择性地汇总了模型参数，以最大程度地提高跨比异质权重的兼容性。我们使用具有不同位低的客户端在基准数据集上的相关FL基准验证了Prowd。我们的prowd在很大程度上优于基线FL算法以及在拟议的BHFL方案下的天真方法（例如，平均分组）。

Information leakage is becoming a critical problem as various information becomes publicly available by mistake, and machine learning models train on that data to provide services. As a result, one's private information could easily be memorized by such trained models. Unfortunately, deleting information is out of the question as the data is already exposed to the Web or third-party platforms. Moreover, we cannot necessarily control the labeling process and the model trainings by other parties either. In this setting, we study the problem of targeted disinformation generation where the goal is to dilute the data and thus make a model safer and more robust against inference attacks on a specific target (e.g., a person's profile) by only inserting new data. Our method finds the closest points to the target in the input space that will be labeled as a different class. Since we cannot control the labeling process, we instead conservatively estimate the labels probabilistically by combining decision boundaries of multiple classifiers using data programming techniques. Our experiments show that a probabilistic decision boundary can be a good proxy for labelers, and that our approach is effective in defending against inference attacks and can scale to large data.

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.

Cellular automata (CA) captivate researchers due to teh emergent, complex individualized behavior that simple global rules of interaction enact. Recent advances in the field have combined CA with convolutional neural networks to achieve self-regenerating images. This new branch of CA is called neural cellular automata [1]. The goal of this project is to use the idea of idea of neural cellular automata to grow prediction machines. We place many different convolutional neural networks in a grid. Each conv net cell outputs a prediction of what the next state will be, and minimizes predictive error. Cells received their neighbors' colors and fitnesses as input. Each cell's fitness score described how accurate its predictions were. Cells could also move to explore their environment and some stochasticity was applied to movement.

There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous navigation stack for the robot pruning a vineyard. The Vinum project is using the quadruped robot HyQReal. This paper introduces an architecture for a quadruped robot to autonomously move through a vineyard by identifying and approaching grapevines for pruning. The higher level control is a state machine switching between searching for destination positions, autonomously navigating towards those locations, and stopping for the robot to complete a task. The destination points are determined by identifying grapevine trunks using instance segmentation from a Mask Region-Based Convolutional Neural Network (Mask-RCNN). These detections are sent through a filter to avoid redundancy and remove noisy detections. The combination of these features is the basis for the proposed architecture.

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.