  In this paper, we explore deep learning methods for estimating when objects
were made. Automatic methods for this task could potentially be useful for
historians, collectors, or any individual interested in estimating when their
artifact was created. Direct applications include large-scale data organization
or retrieval. Toward this goal, we utilize features from existing deep networks
and also fine-tune new networks for temporal estimation. In addition, we create
two new datasets of 67,771 dated clothing items from Flickr and museum
collections. Our method outperforms both a color-based baseline and previous
state of the art methods for temporal estimation. We also provide several
analyses of what our networks have learned, and demonstrate applications to
identifying temporal inspiration in fashion collections.
