Anomaly Detection (AD) is a critical task that involves identifying
observations that do not conform to a learned model of normality. Prior work in
deep AD is predominantly based on a familiarity hypothesis, where familiar
features serve as the reference in a pre-trained embedding space. While this
strategy has proven highly successful, it turns out that it causes consistent
false negatives when anomalies consist of truly novel features that are not
well captured by the pre-trained encoding. We propose a novel approach to AD
using explainability to capture novel features as unexplained observations in
the input space. We achieve strong performance across a wide range of anomaly
benchmarks by combining similarity and novelty in a hybrid approach. Our
approach establishes a new state-of-the-art across multiple benchmarks,
handling diverse anomaly types while eliminating the need for expensive
background models and dense matching. In particular, we show that by taking
account of novel features, we reduce false negative anomalies by up to 40% on
challenging benchmarks compared to the state-of-the-art. Our method gives
visually inspectable explanations for pixel-level anomalies.

使用解释性来捕获输入空间中的新特征作为未解释的观察结果，结合相似性和新颖性的混合方法在各种异常基准上实现了强大的性能，成为多个基准的新的最先进方法，消除了昂贵的背景模型和密集匹配的需求，并对挑战性的基准上的假阴性异常减少了 40%。我们的方法为像素级异常提供可视化的检查解释。

超越熟悉特征的深度异常检测

Going Beyond Familiar Features for Deep Anomaly Detection

Sentiment classification is widely used for product reviews and in online
social media such as forums, Twitter, and blogs. However, the problem of
classifying the sentiment of user comments on news sites has not been addressed
yet. News sites cover a wide range of domains including politics, sports,
technology, and entertainment, in contrast to other online social sites such as
forums and review sites, which are specific to a particular domain. A user
associated with a news site is likely to post comments on diverse topics (e.g.,
politics, smartphones, and sports) or diverse entities (e.g., Obama, iPhone, or
Google). Classifying the sentiment of users tied to various entities may help
obtain a holistic view of their personality, which could be useful in
applications such as online advertising, content personalization, and political
campaign planning. In this paper, we formulate the problem of entity-specific
sentiment classification of comments posted on news articles in Yahoo News and
propose novel features that are specific to news comments. Experimental results
show that our models outperform state-of-the-art baselines.

本文提出了一种针对新闻文章上用户评论进行实体特定的情感分类的方法，并提出了一些与新闻评论相关的特征，实验证明该方法优于现有基线模型。