A plethora of methods have been proposed to explain howdeep neural networks reach a decision but comparativelylittle effort has been made to ensure that the explanationsproduced by these methods are objectively relevant.
Recent advances in the machine learning community allowed different use cases to emerge, as its association to domains like cooking which created the computational cuisine.
Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them.
Our approach is among the three best to tackle the M2CAI Workflow challenge.
Our last contribution is a framework, build on top of Torch7, for training and testing deep models on any visual recognition tasks and on datasets of any scale.