We present Deep Region Competition (DRC), an algorithm designed to extract foreground objects from images in a fully unsupervised manner.

BERT is incapable of processing long texts due to its quadratically increasing memory andtimeconsumption. Themost natural waystoaddress thisproblem, such as slicing the text by a sliding window or simplifying transformers, suffer from insufficient long-range attentions orneed customized CUDAkernels.

In adversarial machine learning, deep neural networks can fit the adversarial examples on the training dataset but have poor generalizationability on the test set.

This paper considers label-level noise in image sensory anomaly detection for thefirsttime.

Graph neural networks (GNNs) have received extensive attention from researchers due to their excellent performance on various graph learning tasks such as social analysis [24, 17, 29], drug discovery [12, 25], traffic forecasting [18, 3, 6], recommendation system [38, 32] and computer vision[39,4].