Novelty detection seeks to identify samples deviating from a known distribution, yet data shifts in a multitude of ways, and only a few consist of relevant changes. Aligned with out-of-distribution generalization literature, we advocate for a formal distinction between task-relevant semantic or content changes and irrelevant style changes. This distinction forms the basis for robust novelty detection, emphasizing the identification of semantic changes resilient to style distributional shifts. To this end, we introduce Stylist, a method that utilizes pretrained large-scale model representations to selectively discard environment-biased features. By computing per-feature scores based on feature distribution distances between environments, Stylist effectively eliminates features responsible for spurious correlations, enhancing novelty detection performance. Evaluations on adapted domain generalization datasets and a synthetic dataset demonstrate Stylist's efficacy in improving novelty detection across diverse datasets with stylistic and content shifts. We make our code available at https://github.com/bit-ml/Stylist.

One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset.Despite generating significant progress in the field, inconsistent performance differences between the closed and open test sets have been reported.To this end, we improve the experimental setup by proposing five new public splits over the PAN dataset, specifically designed to isolate and identify biases related to the text topic and to the author's writing style.We evaluate several BERT-like baselines on these splits, showing that such models are competitive with authorship verification state-of-the-art methods.Furthermore, using explainable AI, we find that these baselines are biased towards named entities.We show that models trained without the named entities obtain better results and generalize better when tested on DarkReddit, our new dataset for authorship verification.Test split O2D2 * O2D2 BERT Naive † Comp.† Closed 93.5 96.4 95.6 75.6 72.2 Clopen 94.0 96.0 97.4 74.1 71.1 Open UA 92.6 92.6 90.2 78.6 68.5 Open UF 91.4 95.1 91.6 79.9 79.0 Open All 80.6 67.5 88.7 75.6 76.9 PAN Closed 93.3 93.5 -74.7 74.2 PAN Open 93.3 94.4 -75.3 74.

Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable scenario for studying the generalization properties of ML models. The existing benchmarks are focused on supervised learning, and to the best of our knowledge, there is none for unsupervised learning. Therefore, we introduce an unsupervised anomaly detection benchmark with data that shifts over time, built over Kyoto-2006+, a traffic dataset for network intrusion detection. This type of data meets the premise of shifting the input distribution: it covers a large time span (10 years), with naturally occurring changes over time (e.g. users modifying their behavior patterns, and software updates). We first highlight the non-stationary nature of the data, using a basic per-feature analysis, t-SNE, and an Optimal Transport approach for measuring the overall distribution distances between years. Next, we propose AnoShift, a protocol splitting the data in IID, NEAR, and FAR testing splits. We validate the performance degradation over time with diverse models, ranging from classical approaches to deep learning. Finally, we show that by acknowledging the distribution shift problem and properly addressing it, the performance can be improved compared to the classical training which assumes independent and identically distributed data (on average, by up to 3% for our approach). Dataset and code are available at https://github.com/bit-ml/AnoShift/.

Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods.Recent deep methods for anomalies in images learn better features of normality in an end-to-end self-supervised setting.These methods train a model to discriminate between different transformations applied to visual data and then use the output to compute an anomaly score.We use this approach for AD in text, by introducing a novel pretext task on text sequences.We learn our DATE model end-to-end, enforcing two independent and complementary self-supervision signals, one at the token-level and one at the sequencelevel.Under this new task formulation, we show strong quantitative and qualitative results on the 20Newsgroups and AG News datasets.In the semi-supervised setting, we outperform state-of-the-art results by +13.5% and +6.9%, respectively (AUROC).In the unsupervised configuration, DATE surpasses all other methods even when 10% of its training data is contaminated with outliers (compared with 0% for the others).

We formulate object segmentation in video as a spectral graph clustering problem in space and time, in which nodes are pixels and their relations form local neighbourhoods. We claim that the strongest cluster in this pixel-level graph represents the salient object segmentation. We compute the main cluster using a novel and fast 3D filtering technique that finds the spectral clustering solution, namely the principal eigenvector of the graph's adjacency matrix, without building the matrix explicitly - which would be intractable. Our method is based on the power iteration which we prove is equivalent to performing a specific set of 3D convolutions in the space-time feature volume. This allows us to avoid creating the matrix and have a fast parallel implementation on GPU. We show that our method is much faster than classical power iteration applied directly on the adjacency matrix. Different from other works, ours is dedicated to preserving object consistency in space and time at the level of pixels. In experiments, we obtain consistent improvement over the top state of the art methods on DAVIS-2016 dataset. We also achieve top results on the well-known SegTrackv2 dataset.

Context-aware computing is a new paradigm that relies on large amounts of data collected from a variety of sources, ranging from smartphones to sensors, to automatically take smart decisions. This usually leads to large volumes of data, that need to be further processed to derive higher-level context information. Clouds have recently emerged as interesting candidates to support the storage and aggregation of such data for large-scale context-aware applications. However, specific extensions to support context-aware data need to be designed in order to be able to fully exploit the clouds' potential. In this paper we introduce such a cloud-based system, designed to support real-time processing and persistent storage of context data. Context Aware Framework is designed as an extension of the BlobSeer storage system, building a context-aware layer on top of it to enable scalable high-throughput under high-concurrency for big context data. Our experimental evaluation validates the transparency, mobility and real-time guarantees provided by our approach to context-aware applications.