Learning-based image hashing consists in turning high-dimensional image features into compact binary codes, while preserving their semantic similarity (i.e., if two images are close in terms of content, their codes should be close as well). In this context, many existing hashing techniques rely on supervision for preserving these semantic properties. In this paper , we aim at learning such binary codes by exploiting the underlying structure of unlabeled data, using deep learning. The proposed deep network is based on a stacked convolu-tional autoencoder which hierarchically maps input images into a low-dimensional space. A binary relaxation constraint applied to the middle layer of the network – the one containing the code – makes the codes sparse and binary. To demonstrate the competitiveness of the proposed architecture, we evaluate the so produced hash codes on image retrieval and image classification tasks on the MNIST dataset, and compare its performance with state-of-the-art approaches.