受到正规彩票假说（RLTH）的启发，该假说假设在密集网络中存在平稳（非二进制）子网，以实现密集网络的竞争性能，我们提出了几个播放类增量学习（FSCIL）方法。 to as \ emph {soft-subnetworks（softnet）}。我们的目标是逐步学习一系列会议，每个会议在每个课程中只包含一些培训实例，同时保留了先前学到的知识。软网络在基本训练会议上共同学习模型权重和自适应非二进制软面具，每个面具由主要和次要子网组成；前者的目的是最大程度地减少训练期间的灾难性遗忘，而后者的目的是避免在每个新培训课程中过度拟合一些样本。我们提供了全面的经验验证，表明我们的软网络通过超越基准数据集的最先进基准的性能来有效地解决了几个弹药的学习问题。

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。

在实际情况下，较大的全局图的子图可以分布在多个设备或机构之间，并且仅由于隐私限制而在本地访问，尽管它们之间可能存在链接。最近，拟议的子图联合学习（FL）方法涉及跨私人本地子图的那些缺失的链接，而分布式培训图形神经网络（GNN）。但是，他们忽略了子图中的不可避免的异质性，这是由包含全球图的不同部分的子图引起的。例如，一个子图可能属于较大的全局图中的一个社区之一。在这种情况下，天真的子图FL将从训练有异质图分布的本地GNN模型中崩溃不相容的知识。为了克服这样的局限性，我们引入了一个新的子图FL问题，即个性化的子图FL，该子图专注于相互关联的本地GNN模型的联合改进，而不是学习一个单一的全球GNN模型，并提出了一个新颖的框架，并提出了一个新型的框架，并提出了一个联合的个性化次级学习（ Fed-pub），以解决它。个性化子图FL中的一个至关重要的挑战是服务器不知道每个客户端具有哪个子图。 Fed-pub因此使用随机图作为输入来计算它们之间的相似性，并使用它们执行对服务器端聚合的加权平均。此外，它在每个客户端学习一个个性化的稀疏掩码，以选择和更新聚合参数的子图相关子集。我们考虑了非重叠和重叠子图的六个数据集中的Fed-Pub在六个数据集上的子图FL性能，我们的基本上要优于相关的基线。

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）。

We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them. We validate DEN on multiple public datasets under lifelong learning scenarios, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch counterparts with substantially fewer number of parameters. Further, the obtained network fine-tuned on all tasks obtained significantly better performance over the batch models, which shows that it can be used to estimate the optimal network structure even when all tasks are available in the first place.

In this paper, we learn a diffusion model to generate 3D data on a scene-scale. Specifically, our model crafts a 3D scene consisting of multiple objects, while recent diffusion research has focused on a single object. To realize our goal, we represent a scene with discrete class labels, i.e., categorical distribution, to assign multiple objects into semantic categories. Thus, we extend discrete diffusion models to learn scene-scale categorical distributions. In addition, we validate that a latent diffusion model can reduce computation costs for training and deploying. To the best of our knowledge, our work is the first to apply discrete and latent diffusion for 3D categorical data on a scene-scale. We further propose to perform semantic scene completion (SSC) by learning a conditional distribution using our diffusion model, where the condition is a partial observation in a sparse point cloud. In experiments, we empirically show that our diffusion models not only generate reasonable scenes, but also perform the scene completion task better than a discriminative model. Our code and models are available at https://github.com/zoomin-lee/scene-scale-diffusion

The deep neural network (DNN) models for object detection using camera images are widely adopted in autonomous vehicles. However, DNN models are shown to be susceptible to adversarial image perturbations. In the existing methods of generating the adversarial image perturbations, optimizations take each incoming image frame as the decision variable to generate an image perturbation. Therefore, given a new image, the typically computationally-expensive optimization needs to start over as there is no learning between the independent optimizations. Very few approaches have been developed for attacking online image streams while considering the underlying physical dynamics of autonomous vehicles, their mission, and the environment. We propose a multi-level stochastic optimization framework that monitors an attacker's capability of generating the adversarial perturbations. Based on this capability level, a binary decision attack/not attack is introduced to enhance the effectiveness of the attacker. We evaluate our proposed multi-level image attack framework using simulations for vision-guided autonomous vehicles and actual tests with a small indoor drone in an office environment. The results show our method's capability to generate the image attack in real-time while monitoring when the attacker is proficient given state estimates.

Iris segmentation is the initial step to identify biometric of animals to establish a traceability system of livestock. In this study, we propose a novel deep learning framework for pixel-wise segmentation with minimum use of annotation labels using BovineAAEyes80 public dataset. In the experiment, U-Net with VGG16 backbone was selected as the best combination of encoder and decoder model, demonstrating a 99.50% accuracy and a 98.35% Dice coefficient score. Remarkably, the selected model accurately segmented corrupted images even without proper annotation data. This study contributes to the advancement of the iris segmentation and the development of a reliable DNNs training framework.

We study grammar induction with mildly context-sensitive grammars for unsupervised discontinuous parsing. Using the probabilistic linear context-free rewriting system (LCFRS) formalism, our approach fixes the rule structure in advance and focuses on parameter learning with maximum likelihood. To reduce the computational complexity of both parsing and parameter estimation, we restrict the grammar formalism to LCFRS-2 (i.e., binary LCFRS with fan-out two) and further discard rules that require O(n^6) time to parse, reducing inference to O(n^5). We find that using a large number of nonterminals is beneficial and thus make use of tensor decomposition-based rank-space dynamic programming with an embedding-based parameterization of rule probabilities to scale up the number of nonterminals. Experiments on German and Dutch show that our approach is able to induce linguistically meaningful trees with continuous and discontinuous structures

This work proposes a framework developed to generalize Critical Heat Flux (CHF) detection classification models using an Unsupervised Image-to-Image (UI2I) translation model. The framework enables a typical classification model that was trained and tested on boiling images from domain A to predict boiling images coming from domain B that was never seen by the classification model. This is done by using the UI2I model to transform the domain B images to look like domain A images that the classification model is familiar with. Although CNN was used as the classification model and Fixed-Point GAN (FP-GAN) was used as the UI2I model, the framework is model agnostic. Meaning, that the framework can generalize any image classification model type, making it applicable to a variety of similar applications and not limited to the boiling crisis detection problem. It also means that the more the UI2I models advance, the better the performance of the framework.