The neural radiance field (NeRF) has made significant strides in representing
3D scenes and synthesizing novel views. Despite its advancements, the high
computational costs of NeRF have posed challenges for its deployment in
resource-constrained environments and real-time applications. As an alternative
to NeRF-like neural rendering methods, 3D Gaussian Splatting (3DGS) offers
rapid rendering speeds while maintaining excellent image quality. However, as
it represents objects and scenes using a myriad of Gaussians, it requires
substantial storage to achieve high-quality representation. To mitigate the
storage overhead, we propose Factorized 3D Gaussian Splatting (F-3DGS), a novel
approach that drastically reduces storage requirements while preserving image
quality. Inspired by classical matrix and tensor factorization techniques, our
method represents and approximates dense clusters of Gaussians with
significantly fewer Gaussians through efficient factorization. We aim to
efficiently represent dense 3D Gaussians by approximating them with a limited
amount of information for each axis and their combinations. This method allows
us to encode a substantially large number of Gaussians along with their
essential attributes -- such as color, scale, and rotation -- necessary for
rendering using a relatively small number of elements. Extensive experimental
results demonstrate that F-3DGS achieves a significant reduction in storage
costs while maintaining comparable quality in rendered images.

通过矩阵和张量分解技术，我们提出了一种新方法，即因式化三维高斯粒子点描（F-3DGS），通过高效的因式化，大大减少了存储需求，同时保持了渲染图像的质量。

F-3DGS: 三维高斯点云投影的分解坐标和表示

F-3DGS: Factorized Coordinates and Representations for 3D Gaussian  Splatting

In this paper, we explore the use of metric learning to embed Windows PE
files in a low-dimensional vector space for downstream use in a variety of
applications, including malware detection, family classification, and malware
attribute tagging. Specifically, we enrich labeling on malicious and benign PE
files using computationally expensive, disassembly-based malicious
capabilities. Using these capabilities, we derive several different types of
metric embeddings utilizing an embedding neural network trained via contrastive
loss, Spearman rank correlation, and combinations thereof. We then examine
performance on a variety of transfer tasks performed on the EMBER and SOREL
datasets, demonstrating that for several tasks, low-dimensional,
computationally efficient metric embeddings maintain performance with little
decay, which offers the potential to quickly retrain for a variety of transfer
tasks at significantly reduced storage overhead. We conclude with an
examination of practical considerations for the use of our proposed embedding
approach, such as robustness to adversarial evasion and introduction of
task-specific auxiliary objectives to improve performance on mission critical
tasks.

本研究旨在探讨采用度量学习将 Windows PE 文件嵌入到低维向量空间，以用于恶意软件检测、家族分类和恶意软件属性标记等应用程序，并通过比较弱相关性和计算负载等多种度量嵌入的方式，研究其性能在不同的转移任务中。实验结果表明，在一些任务中，这种低维度的嵌入方法维持了较好的性能并具备重训练能力，从而降低了存储开销。此外，我们还讨论了该方法的实际应用考虑事项，如对抗性逃避的鲁棒性和任务特定辅助目标的引入以提高关键任务的性能。