Weight-sharing quantization has emerged as a technique to reduce energy
expenditure during inference in large neural networks by constraining their
weights to a limited set of values. However, existing methods for
weight-sharing quantization often make assumptions about the treatment of
weights based on value alone that neglect the unique role weight position
plays. This paper proposes a probabilistic framework based on Bayesian neural
networks (BNNs) and a variational relaxation to identify which weights can be
moved to which cluster centre and to what degree based on their individual
position-specific learned uncertainty distributions. We introduce a new
initialisation setting and a regularisation term which allow for the training
of BNNs under complex dataset-model combinations. By leveraging the flexibility
of weight values captured through a probability distribution, we enhance noise
resilience and downstream compressibility. Our iterative clustering procedure
demonstrates superior compressibility and higher accuracy compared to
state-of-the-art methods on both ResNet models and the more complex
transformer-based architectures. In particular, our method outperforms the
state-of-the-art quantization method top-1 accuracy by 1.6% on ImageNet using
DeiT-Tiny, with its 5 million+ weights now represented by only 296 unique
values.

该论文提出了一种基于贝叶斯神经网络（BNNs）和变分松弛的概率性框架，用于确定可以将哪些权重转移到哪个聚类中心以及在多大程度上，以提高权重共享量化方法的压缩性能和准确性。

概率性权重修正：用于量化的神经网络权重不确定性的大规模训练

Probabilistic Weight Fixing: Large-scale training of neural network  weight uncertainties for quantization

Instance Segmentation, which seeks to obtain both class and instance labels
for each pixel in the input image, is a challenging task in computer vision.
State-of-the-art algorithms often employ two separate stages, the first one
generating object proposals and the second one recognizing and refining the
boundaries. Further, proposals are usually based on detectors such as faster
R-CNN which search for boxes in the entire image exhaustively. In this paper,
we propose a novel algorithm that directly utilizes a fully convolutional
network (FCN) to predict instance labels. Specifically, we propose a
variational relaxation of instance segmentation as minimizing an optimization
functional for a piecewise-constant segmentation problem, which can be used to
train an FCN end-to-end. It extends the classical Mumford-Shah variational
segmentation problem to be able to handle permutation-invariant labels in the
ground truth of instance segmentation. Experiments on PASCAL VOC 2012, Semantic
Boundaries dataset(SBD), and the MSCOCO 2017 dataset show that the proposed
approach efficiently tackle the instance segmentation task. The source code and
trained models will be released with the paper.

本文提出了一种利用全卷积神经网络直接预测实例标签的方法，并将分割问题作为最小化优化函数的变分松弛问题来处理，并扩展了经典的 Mumford-Shah 变分分割问题以处理实例分割中的置换不变标签，实验证明此方法能有效地解决实例分割任务。