We explore the problem of generating minority samples using diffusion models.
The minority samples are instances that lie on low-density regions of a data
manifold. Generating sufficient numbers of such minority instances is
important, since they often contain some unique attributes of the data.
However, the conventional generation process of the diffusion models mostly
yields majority samples (that lie on high-density regions of the manifold) due
to their high likelihoods, making themselves highly ineffective and
time-consuming for the task. In this work, we present a novel framework that
can make the generation process of the diffusion models focus on the minority
samples. We first provide a new insight on the majority-focused nature of the
diffusion models: they denoise in favor of the majority samples. The
observation motivates us to introduce a metric that describes the uniqueness of
a given sample. To address the inherent preference of the diffusion models
w.r.t. the majority samples, we further develop minority guidance, a sampling
technique that can guide the generation process toward regions with desired
likelihood levels. Experiments on benchmark real datasets demonstrate that our
minority guidance can greatly improve the capability of generating the
low-likelihood minority samples over existing generative frameworks including
the standard diffusion sampler.

本论文探讨用扩散模型生成少数族裔样本的问题，提出了一种新的框架来引导扩散模型的生成过程，侧重于生成低密度区域上的少数族裔样本。实验结果表明，与标准扩散采样器相比，我们的少数族裔引导技术可以极大地提高生成低概率少数族裔样本的能力。

不偏袒：扩散模型的少数族裔指导

Don't Play Favorites: Minority Guidance for Diffusion Models

Modern object detectors have achieved impressive progress under the close-set
setup. However, open-set object detection (OSOD) remains challenging since
objects of unknown categories are often misclassified to existing known
classes. In this work, we propose to identify unknown objects by separating
high/low-density regions in the latent space, based on the consensus that
unknown objects are usually distributed in low-density latent regions. As
traditional threshold-based methods only maintain limited low-density regions,
which cannot cover all unknown objects, we present a novel Open-set Detector
(OpenDet) with expanded low-density regions. To this aim, we equip OpenDet with
two learners, Contrastive Feature Learner (CFL) and Unknown Probability Learner
(UPL). CFL performs instance-level contrastive learning to encourage compact
features of known classes, leaving more low-density regions for unknown
classes; UPL optimizes unknown probability based on the uncertainty of
predictions, which further divides more low-density regions around the cluster
of known classes. Thus, unknown objects in low-density regions can be easily
identified with the learned unknown probability. Extensive experiments
demonstrate that our method can significantly improve the OSOD performance,
e.g., OpenDet reduces the Absolute Open-Set Errors by 25%-35% on six OSOD
benchmarks. Code is available at: this https URL

提出了一种基于对潜在空间中低密度区域的分离，通过 OpenDet 具有扩展的低密度区域来识别未知物体的方法，此方法包括一个对已知类别进行对比特征学习的 CFL 及一个基于预测的不确定性优化未知概率的 UPL，实验证明此方法可以显著提高 OSOD 性能。

扩展低密度潜在区域以进行开放式物体检测

Expanding Low-Density Latent Regions for Open-Set Object Detection

Consistency regularization describes a class of approaches that have yielded
ground breaking results in semi-supervised classification problems. Prior work
has established the cluster assumption - under which the data distribution
consists of uniform class clusters of samples separated by low density regions
- as important to its success. We analyze the problem of semantic segmentation
and find that its' distribution does not exhibit low density regions separating
classes and offer this as an explanation for why semi-supervised segmentation
is a challenging problem, with only a few reports of success. We then identify
choice of augmentation as key to obtaining reliable performance without such
low-density regions. We find that adapted variants of the recently proposed
CutOut and CutMix augmentation techniques yield state-of-the-art
semi-supervised semantic segmentation results in standard datasets.
Furthermore, given its challenging nature we propose that semantic segmentation
acts as an effective acid test for evaluating semi-supervised regularizers.
Implementation at: this https URL

在分析语义分割问题时，我们发现其分布没有展现出低密度区域，并将其作为解释半监督分割为何是具有挑战性的问题的原因，然后找到增强选择作为获得可靠性性能的关键，并发现这些改进后的 CutOut 和 CutMix 增强技术在标准数据集中提供了最先进的半监督语义分割结果。