Federated learning is a recently proposed distributed machine learning paradigm for privacy
preservation, which has found a wide range of applications where data privacy is of primary …

We review the problem of automating hardware-aware architectural design process of Deep
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …

Learning with few labeled data has been a longstanding problem in the computer vision and
machine learning research community. In this paper, we introduced a new semi-supervised …

Unlike existing knowledge distillation methods focus on the baseline settings, where the
teacher models and training strategies are not that strong and competing as state-of-the-art …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Abstract Knowledge distillation is a widely used paradigm for inheriting information from a
complicated teacher network to a compact student network and maintaining the strong …

Unsupervised visual representation learning has gained much attention from the computer
vision community because of the recent achievement of contrastive learning. Most of the …

Recently neural architecture (NAS) search has attracted great interest in academia and
industry. It remains a challenging problem due to the huge search space and computational …

In this paper, we present Auto-KD, the first automated search framework for optimal
knowledge distillation design. Traditional distillation techniques typically require handcrafted …

Determining which image regions to concentrate is critical for Human-Object Interaction
(HOI) detection. Conventional HOI detectors focus on either detected human and object …