4 accepted papers
We propose EVOlutionary Selector (EVOS), an efficient training paradigm for accelerating Implicit Neural Representation (INR). Unlike conventional INR training that feeds all samples through the neural network in each iteration, our approach restricts training to strategically selected points, reduc…
We propose symmetric power transformation to enhance the capacity of Implicit Neural Representation (INR) from the perspective of data transformation. Unlike prior work utilizing random permutation or index rearrangement, our method features a reversible operation that does not require additional st…
Respiratory sound classification plays a pivotal role in diagnosing respiratory diseases. While deep learning models have succeeded with various respiratory sound datasets, our experiments indicate that models trained on one dataset often fail to generalize effectively to others, mainly due to data…
Automation of pulmonary disease identification has been a long-standing area of research and gained increased attention after the COVID-19 pandemic. However, existing respiratory sound classification algorithms exhibit significant limitations, including suboptimal performance, insufficient input rob…