Anomaly detection methods require high-quality features. One way of obtaining strong features is to adapt pre-trained features to anomaly detection on the target distribution. Unfortunately, simple adaptation methods often result in catastrophic collapse (feature deterioration) and reduce performance. DeepSVDD combats collapse by removing biases from architectures, but this limits the adaptation performance gain. In this work, we propose two methods for combating collapse: i) a variant of early stopping that dynamically learns the stopping iteration ii) elastic regularization inspired by continual learning. In addition, we conduct a thorough investigation of Imagenet-pretrained features for one-class anomaly detection. Our method, PANDA, outperforms the state-of-the-art in the one-class and outlier exposure settings (CIFAR10: 96.2% vs. 90.1% and 98.9% vs. 95.6%).
Deep anomaly detection methods learn representations that separate between normal and anomalous samples. Very effective representations are obtained when powerful externally trained feature extractors (e.g. ResNets pre-trained on Ima-geNet) are fine-tuned on the training data which consists of normal samples and no anomalies. However, this is a difficult task that can suffer from catastrophic collapse, i.e. it is prone to learning trivial and non-specific features. In this paper, we propose a new loss function which can overcome failure modes of both center-loss and contrastive-loss methods. Furthermore, we combine it with a confidence-invariant angular center loss, which replaces the Euclidean distance used in previous work, that was sensitive to prediction confidence. Our improvements yield a new anomaly detection approach, based on Mean-Shifted Contrastive Loss, which is both more accurate and less sensitive to catastrophic collapse than previous methods. Our method achieves state-of-the-art anomaly detection performance on multiple benchmarks including 97.5% ROC-AUC on the CIFAR-10 dataset 1 . IntroductionAnomaly detection is a fundamental task for intelligent agents that aims to detect if an observed pattern is normal or anomalous (unusual or unlikely). Anomaly detection has broad applications in scientific and industrial tasks such as detecting new physical phenomena (black holes, supernovae) or genetic mutations, as well as production line inspection and video surveillance. Due to the significance of the task, many efforts have been focused on automatic anomaly detection, particularly on statistical and machine learning methods. A common paradigm used by many anomaly detection methods is measuring the probability of samples and assigning high-probability samples as normal and low-probability samples as anomalous. The quality of the density estimators is closely related to the quality of features used to represent the data. Classical methods used statistical estimators such as K nearest-neighbors (kNN) or Gaussian mixture models (GMMs) on raw features, however this often results in sub-optimal results on high-dimensional data such as images. Many recent methods, learn features in a self-supervised way and use them in order to detect anomalies. Their main weakness is that anomaly detection datasets are typically small and do not include anomalous samples resulting in weak features. An alternative direction, which achieved better results, is to transfer features learned from auxiliary tasks on large-scale external datasets such as ImageNet classification. It was found that fine-tuning the pre-trained features on the normal training data can result in significant performance improvements, however it is quite challenging. The main issue with fine-tuning on one-class classification (OCC) tasks such as anomaly detection is catastrophic collapse i.e. after an initial improvement in efficacy, the features degrade and become uninformative. This phenomenon is caused by trivial solutions allowed by OCC tasks such as t...
Deep anomaly detection methods learn representations that separate between normal and anomalous images. Although self-supervised representation learning is commonly used, small dataset sizes limit its effectiveness. It was previously shown that utilizing external, generic datasets (e.g. ImageNet classification) can significantly improve anomaly detection performance. One approach is outlier exposure, which fails when the external datasets do not resemble the anomalies. We take the approach of transferring representations pre-trained on external datasets for anomaly detection. Anomaly detection performance can be significantly improved by fine-tuning the pre-trained representations on the normal training images. In this paper, we first demonstrate and analyze that contrastive learning, the most popular self-supervised learning paradigm cannot be naively applied to pre-trained features. The reason is that pre-trained feature initialization causes poor conditioning for standard contrastive objectives, resulting in bad optimization dynamics. Based on our analysis, we provide a modified contrastive objective, the Mean-Shifted Contrastive Loss. Our method is highly effective and achieves a new state-of-the-art anomaly detection performance including 98.6% ROC-AUC on the CIFAR-10 dataset.
Using multiple monitors is commonly thought to improve productivity, but this is hard to check experimentally. We use a survey, taken by 101 practitioners of which 80% have coded professionally for at least 2 years, to assess subjective perspectives based on experience. To improve validity, we compare situations in which developers naturally use different setups-the difference between working at home or at the office, and how things changed when developers were forced to work from home due to the Covid-19 pandemic. The results indicate that using multiple monitors is indeed perceived as beneficial and desirable. 19% of the respondents reported adding a monitor to their home setup in response to the Covid-19 situation. At the same time, the single most influential factor cited as affecting productivity was not the physical setup but interactions with co-workers-both reduced productivity due to lack of connections available at work, and improved productivity due to reduced interruptions from coworkers. A central implication of our work is that empirical research on software development should be conducted in settings similar to those actually used by practitioners, and in particular using workstations configured with multiple monitors.
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