Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …

Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

In this paper, we propose a novel cross-modal distillation method, called TinyCLIP, for large-
scale language-image pre-trained models. The method introduces two core techniques …

Most works on person re-identification (ReID) take advantage of large backbone networks
such as ResNet, which are designed for image classification instead of ReID, for feature …

Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are
placed in various fields. Many of these devices are based on machine learning (ML) models …

Channel pruning has been broadly recognized as an effective technique to reduce the
computation and memory cost of deep convolutional neural networks. However …

Edge computing and artificial intelligence (AI), especially deep learning algorithms, are
gradually intersecting to build the novel system, namely edge intelligence. However, the …

Budgeted pruning is the problem of pruning under resource constraints. In budgeted
pruning, how to distribute the resources across layers (ie, sparsity allocation) is the key …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Abstract Model compression techniques are recently gaining explosive attention for
obtaining efficient AI models for various real time applications. Channel pruning is one …