Deep anomaly detection aims to provide robust and efficient classifiers for zero-shot (unsupervised, UNS) and few-shot (imbalanced supervised, IMS) settings. However, current models still struggle on edge-case normal samples and are often unable to keep high performance over different scales of anomalies. Additionally, there is a lack of a unified framework that efficiently addresses both UNS and IMS settings. To address these limitations, we present a novel two-stage method which leverages multi-scale normal prototypes during training to compute an anomaly deviation score. First, we employ a novel memory-augmented contrastive learning to jointly learn representations and memory modules across multiple scales. This allows us to effectively capture subtle features of normal data while adapting to varying levels of anomaly complexity. Then, we train an efficient anomaly distance-based detector that computes spatial deviation maps between the learned prototypes and incoming observations. Our model outperforms the SoTA on a wide range of anomalies, including object, style, and local anomalies, as well as industrial inspection and face anti-spoofing, while being on par with SoTa out-of-distribution detectors. Notably, it stands as the first model capable of maintaining exceptional performance across both settings.

Anomaly detection is important in many real-life applications. Recently, self-supervised learning has greatly helped deep anomaly detection by recognizing several geometric transformations. However these methods lack finer features, usually highly depend on the anomaly type, and do not perform well on fine-grained problems. To address these issues, we first introduce in this work three novel and efficient discriminative and generative tasks which have complementary strength: (i) a piece-wise jigsaw puzzle task focuses on structure cues; (ii) a tint rotation recognition is used within each piece, taking into account the colorimetry information; (iii) and a partial re-colorization task considers the image texture. In order to make the re-colorization task more object-oriented than background-oriented, we propose to include the contextual color information of the image border via an attention mechanism. We then present a new out-of-distribution detection function and highlight its better stability compared to existing methods. Along with it, we also experiment different score fusion functions. Finally, we evaluate our method on an extensive protocol composed of various anomaly types, from object anomalies, style anomalies with fine-grained classification to local anomalies with face anti-spoofing datasets. Our model significantly outperforms state-of-the-art with up to 36% relative error improvement on object anomalies and 40% on face anti-spoofing problems.

Deep anomaly detection has become over the years an appealing solution in many fields, and has seen many recent developments. One of the most promising avenues is the use of pretext tasks, which have greatly improved one-class anomaly detection. However this approach is limited by the lack of anomalous samples and carries an important inductive bias. Indeed we could further improve the discrimination power of pretext tasks by incorporating a small set of anomalies, which in practice is often available.To this end, we introduce the concept of learnable pretext tasks, where a pretext task itself is learned to succeed on normal samples while failing on anomalies. To our knowledge it is the first work to explore this direction. By applying the learnable task on a thin plate transform recognition task, our method helps discriminating harder edge-case anomalies and greatly improves anomaly detection. It outperforms state-of-the-art with up to 49% relative error reduction measured with AUROC on various anomaly detection problems including one-vs-all and face presentation attack detection.

Deep anomaly detection has recently seen significant developments to provide robust and efficient classifiers using only a few anomalous samples. Many of those models consist in a first isolated step of representation learning. However, in its current form the learned representation does not encode the semantics of normal sample and anomalies. Indeed during the first step these models will not utilize the available normal/anomaly labels, harming the downstream anomaly detection classifier performances.In the light of this limitation, we introduce a new deep anomaly detector enforcing an anomaly distance constraint on the norm of the representations while using contrastive learning on the direction of the features. This allows it to learn representations well-suited to anomaly detection while avoiding any representation collapse. Moreover, we introduce two strategies of anomaly enriching to improve the robustness of any distance-based anomaly detector. Our model highly improves the state-of-the-art performances on a wide array of anomaly types with up to 74% error relative improvement on object anomalies.

Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not perform well on fine-grained problems since they lack finer features. By combining both high-scale shape features and low-scale fine features in a multi-task framework, our method greatly improves fine-grained anomaly detection. It outperforms state-of-the-art with up to 31% relative error reduction measured with AUROC on various anomaly detection problems including one-vs-all, out-of-distribution detection and face presentation attack detection.