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Context matters! Nevertheless, there has not been much research in exploiting contextual information in deep neural networks. For most part, the entire usage of contextual information has been limited to recurrent neural networks. Attention models and capsule networks are two recent ways of introducing contextual information in non-recurrent models, however both of these algorithms have been developed after this work has started. In this thesis, we show that contextual information can be exploited in 2 fundamentally different ways: implicitly and explicitly. In the DeepScore project, where the usage of context is very important for the recognition of many tiny objects, we show that by carefully crafting convolutional architectures, we can achieve state-of-the-art results, while also being able to implicitly correctly distinguish between objects which are virtually identical, but have different meanings based on their surrounding. In parallel, we show that by explicitly designing algorithms (motivated from graph theory and game theory) that take into considerations the entire structure of the dataset, we can achieve state-of-the-art results in different topics like semi-supervised learning and similarity learning. To the best of our knowledge, we are the first to integrate graph-theoretical modules, carefully crafted for the problem of similarity learning and that are designed to consider contextual information, not only outperforming the other models, but also gaining a speed improvement while using a smaller number of parameters.