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One of the goals of the ICML workshop on representation and learning is toestablish benchmark scores for a new data set of labeled facial expressions.This paper presents the performance of a "Null" model consisting ofconvolutions with random weights, PCA, pooling, normalization, and a linearreadout. Our approach focused on hyperparameter optimization rather than novelmodel components. On the Facial Expression Recognition Challenge held by theKaggle website, our hyperparameter optimization approach achieved a score of60% accuracy on the test data. This paper also introduces a new ensembleconstruction variant that combines hyperparameter optimization with theconstruction of ensembles. This algorithm constructed an ensemble of fourmodels that scored 65.5% accuracy. These scores rank 12th and 5th respectivelyamong the 56 challenge participants. It is worth noting that our approach wasdeveloped prior to the release of the data set, and applied withoutmodification; our strong competition performance suggests that the TPEhyperparameter optimization algorithm and domain expertise encoded in our Nullmodel can generalize to new image classification data sets.

Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be?