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Found 9 papers, 5 papers with code
Gated Cross-Attention Network for Depth Completion
With a simple yet efficient gating mechanism, our proposed method achieves fast and accurate depth completion without the need for additional branches or post-processing steps.
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals.
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
Our one-class classifier is calibrated in two ways: (1) by adaptively penalizing uncertain predictions, which helps eliminate the impact of anomaly contamination while accentuating the predictions that the one-class model is confident in, and (2) by discriminating the normal samples from native anomaly examples that are generated to simulate genuine time series abnormal behaviors on the basis of original data.
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers.
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network
We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded.
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning
Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks.
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning
The majority of existing few-shot learning methods describe image relations with binary labels.
STA: Adversarial Attacks on Siamese Trackers
On this basis, we formulate the adversarial generation problem and propose an end-to-end pipeline to generate a perturbed texture map for the 3D object that causes the trackers to fail.
