Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

Vision transformers have shown great success due to their high model capabilities.
However, their remarkable performance is accompanied by heavy computation costs, which …

In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by
generative adversarial networks (GAN). To compensate for the lack of training samples in …

Compressive autoencoders (CAEs) play an important role in deep learning-based image
compression, but large-scale CAEs are computationally expensive. We propose a …

The network attack detection model based on machine learning (ML) has received extensive
attention and research in PMU measurement data protection of power systems. However …

Performance predictors are used to reduce architecture evaluation costs in neural
architecture search, which however suffers from a large amount of budget consumption in …

Recent works have shown that the computational efficiency of video recognition can be
significantly improved by reducing the spatial redundancy. As a representative work, the …

Recent advances in vision Transformers (ViTs) have come with a voracious appetite for
computing power, high-lighting the urgent need to develop efficient training methods for …

Video Anomaly detection, aiming to detect the abnormal behaviors in surveillance videos, is
a challenging task since the anomalous events are diversified and complicated in different …

Recently, a surge of interest in visual transformers is to reduce the computational cost by
limiting the calculation of self-attention to a local window. Most current work uses a fixed …