Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Network pruning is widely used for reducing the heavy inference cost of deep models in low-
resource settings. A typical pruning algorithm is a three-stage pipeline, ie, training (a large …

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their
low computational budgets constrain both the depth (number of convolution layers) and the …

We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We
start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution …

Convolutional neural networks have been widely deployed in various application scenarios.
In order to extend the applications' boundaries to some accuracy-crucial domains …

We introduce A-ViT, a method that adaptively adjusts the inference cost of vision transformer
ViT for images of different complexity. A-ViT achieves this by automatically reducing the …

Vision Transformers (ViTs) take all the image patches as tokens and construct multi-head
self-attention (MHSA) among them. Complete leverage of these image tokens brings …

Overparameterized transformer networks have obtained state of the art results in various
natural language processing tasks, such as machine translation, language modeling, and …