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It is an innate ability for humans to imagine something only according totheir impression, without having to memorize all the details of what they haveseen. In this work, we would like to demonstrate that a trained convolutionalneural network also has the capability to "remember" its input images. Toachieve this, we propose a simple but powerful framework to establish an{\emph{Impression Space}} upon an off-the-shelf pretrained network. Thisnetwork is referred to as the {\emph{Template Network}} because its filterswill be used as templates to reconstruct images from the impression. In ourframework, the impression space and image space are bridged by a layer-wiseencoding and iterative decoding process. It turns out that the impression spaceindeed captures the salient features from images, and it can be directlyapplied to tasks such as unpaired image translation and image synthesis throughimpression matching without further network training. Furthermore, theimpression naturally constructs a high-level common space for different data.Based on this, we propose a mechanism to model the data relations inside theimpression space, which is able to reveal the feature similarity betweenimages. Our code will be released.

Impression Space from Deep Template Network