用联想记忆设计新损失函数，让深度聚类更连贯高效
Deep Clustering with Associative Memories
- 引入基于能量的联想记忆机制构建联合优化目标
- 在图像与文本数据上均提升聚类效果，适配多种网络结构
- 突破传统方法中表征学习与聚类分离的局限
深度聚类——联合表示学习与潜在空间聚类——是深度学习框架下计算机视觉与文本处理中的经典问题。尽管表示学习通常可微，但聚类本质上是离散优化任务，需依赖近似与正则化才能嵌入标准可微流程，导致表征学习与聚类之间存在割裂。本文提出一种新型损失函数，通过联想记忆（Associative Memories）实现基于能量的动力学建模，构建全新的深度聚类方法DCAM，将表征学习与聚类紧密结合于单一目标函数中。实验表明，DCAM在多种网络架构（卷积、残差、全连接）和数据模态（图像或文本）下均表现出更优的聚类质量。
原文摘要 · Abstract (English)
Deep clustering - joint representation learning and latent space clustering - is a well studied problem especially in computer vision and text processing under the deep learning framework. While the representation learning is generally differentiable, clustering is an inherently discrete optimization task, requiring various approximations and regularizations to fit in a standard differentiable pipeline. This leads to a somewhat disjointed representation learning and clustering. In this work, we propose a novel loss function utilizing energy-based dynamics via Associative Memories to formulate a new deep clustering method, DCAM, which ties together the representation learning and clustering aspects more intricately in a single objective. Our experiments showcase the advantage of DCAM, producing improved clustering quality for various architecture choices (convolutional, residual or fully-connected) and data modalities (images or text).
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