Recent studies give more attention to the anomaly detection (AD) methods that
can leverage a handful of labeled anomalies along with abundant unlabeled data.
These existing anomaly-informed AD methods rely on manually predefined score
target(s), e.g., prior constant or margin hyperparameter(s), to realize
discrimination in anomaly scores between normal and abnormal data. However,
such methods would be vulnerable to the existence of anomaly contamination in
the unlabeled data, and also lack adaptation to different data scenarios. In
this paper, we propose to optimize the anomaly scoring function from the view
of score distribution, thus better retaining the diversity and more
fine-grained information of input data, especially when the unlabeled data
contains anomaly noises in more practical AD scenarios. We design a novel loss
function called Overlap loss that minimizes the overlap area between the score
distributions of normal and abnormal samples, which no longer depends on prior
anomaly score targets and thus acquires adaptability to various datasets.
Overlap loss consists of Score Distribution Estimator and Overlap Area
Calculation, which are introduced to overcome challenges when estimating
arbitrary score distributions, and to ensure the boundness of training loss. As
a general loss component, Overlap loss can be effectively integrated into
multiple network architectures for constructing AD models. Extensive
experimental results indicate that Overlap loss based AD models significantly
outperform their state-of-the-art counterparts, and achieve better performance
on different types of anomalies.

本文提出了一种称为 Overlap loss 的新的损失函数，通过优化异常评分函数的得分分布来更好地保留输入数据的多样性和更细粒度的信息，充分考虑数据干扰和不同数据情况下的自适应性，可以有效地集成到多种网络体系结构中用于 AD 模型的构建，且在各类型异常检测上优于现有技术。

使用分数分布差异进行异常检测

Anomaly Detection with Score Distribution Discrimination

In this work, we propose a novel meta-learning approach for few-shot
classification, which learns transferable prior knowledge across tasks and
directly produces network parameters for similar unseen tasks with training
samples. Our approach, called LGM-Net, includes two key modules, namely,
TargetNet and MetaNet. The TargetNet module is a neural network for solving a
specific task and the MetaNet module aims at learning to generate functional
weights for TargetNet by observing training samples. We also present an
intertask normalization strategy for the training process to leverage common
information shared across different tasks. The experimental results on Omniglot
and miniImageNet datasets demonstrate that LGM-Net can effectively adapt to
similar unseen tasks and achieve competitive performance, and the results on
synthetic datasets show that transferable prior knowledge is learned by the
MetaNet module via mapping training data to functional weights. LGM-Net enables
fast learning and adaptation since no further tuning steps are required
compared to other meta-learning approaches.

本文提出了一种新的元学习方法：LGM-Net，通过学习跨任务的可转移先验知识并直接产生相似未见过任务的网络参数来进行少样本分类，其中包含 TargetNet 模块和 MetaNet 模块，且介绍了一种跨任务整合策略以利用不同任务之间的公共信息，实验结果表明，LGM-Net 可以有效地适应相似未见的任务，具有比其他元学习方法更快的学习和适应速度。

LGM-Net: 学习生成匹配网络进行少样本学习

LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning

Building deep reinforcement learning agents that can generalize and adapt to
unseen environments remains a fundamental challenge for AI. This paper
describes progresses on this challenge in the context of man-made environments,
which are visually diverse but contain intrinsic semantic regularities. We
propose a hybrid model-based and model-free approach, LEArning and Planning
with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on
visual inputs, and a Bayesian model over semantic structures. When placed in an
unseen environment, the agent plans with the semantic model to make high-level
decisions, proposes the next sub-target for the sub-policy to execute, and
updates the semantic model based on new observations. We perform experiments in
visual navigation tasks using House3D, a 3D environment that contains diverse
human-designed indoor scenes with real-world objects. LEAPS outperforms strong
baselines that do not explicitly plan using the semantic content.

本论文提出了一种基于深度强化学习的混合模型与无模型集成的方法 LEAPS，通过对含有内在语义规律但视觉多样性的人造环境的实验表明其比不考虑语义内容的强基线更有效。