机器学习的可解释性：最新进展与未来展望
Interpretability of Machine Learning: Recent Advances and Future Prospects
AI总结：
本文针对机器学习黑箱特性阻碍表征学习的问题，综述了机器学习可解释性的最新进展与未来展望，结合多媒体计算领域的多个应用实例，指出该方向是值得深入投入的重要研究方向。
摘要：
机器学习（ML）的蓬勃发展引发了人们对文本、图像、音频、视频等各类多媒体内容研究的空前关注。随之而来的是，理解与学习基于机器学习的表征已成为智能多媒体研究与应用中知识发现的核心议题。然而，当代机器学习尤其是深度神经网络（DNNs）的黑箱特性，给基于机器学习的表征学习带来了首要挑战。为解决这一黑箱问题，近年来机器学习可解释性研究吸引了大量关注。本文对机器学习可解释性的最新进展与未来展望展开综述，并列举了多个与多媒体计算相关的应用实例，涵盖文本-图像跨模态表征学习、人脸识别以及物体识别。研究明确表明，机器学习可解释性研究是一个极具前景的重要研究方向，值得进一步投入研究资源。
英文摘要：
The proliferation of machine learning (ML) has drawn unprecedented interest in the study of various multimedia contents such as text, image, audio and video, among others. Consequently, understanding and learning ML-based representations have taken center stage in knowledge discovery in intelligent multimedia research and applications. Nevertheless, the black-box nature of contemporary ML, especially in deep neural networks (DNNs), has posed a primary challenge for ML-based representation learning. To address this black-box problem, the studies on interpretability of ML have attracted tremendous interests in recent years. This paper presents a survey on recent advances and future prospects on interpretability of ML, with several application examples pertinent to multimedia computing, including text-image cross-modal representation learning, face recognition, and the recognition of objects. It is evidently shown that the study of interpretability of ML promises an important research direction, one which is worth further investment in.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
