Dataset pruning for targeted knowledge distillation

In this technical report, we describe our submission for Task 1 Data-Efficient Low-Complexity Acoustic Scene Classification [1]. We adopt the baseline model and add a specialised knowledge distillation process before proceeding with the baseline training process. Our...

Message-Passing on Directed Acyclic Graphs Prevents Over-Smoothing

Despite the rising popularity of message-passing neural networks (MPNNs), their ability to fit complex functions over graphs is limited as node representations become more similar with increasing depth—a phenomenon known as over-smoothing. Most approaches to mitigate...

OoDIS: Anomaly Instance Segmentation Benchmark

Safe navigation of self-driving cars and robots requires a precise understanding of their environment. Training data for perception systems cannot cover the wide variety of objects that may appear during deployment. Thus, reliable identification of unknown objects,...