  Traditional machine learning algorithms using hand-crafted feature extraction
techniques (such as local binary pattern) have limited accuracy because of high
variation in images of the same class (or intra-class variation) for food
recognition task. In recent works, convolutional neural networks (CNN) have
been applied to this task with better results than all previously reported
methods. However, they perform best when trained with large amount of annotated
(labeled) food images. This is problematic when obtained in large volume,
because they are expensive, laborious and impractical. Our work aims at
developing an efficient deep CNN learning-based method for food recognition
alleviating these limitations by using partially labeled training data on
generative adversarial networks (GANs). We make new enhancements to the
unsupervised training architecture introduced by Goodfellow et al. (2014),
which was originally aimed at generating new data by sampling a dataset. In
this work, we make modifications to deep convolutional GANs to make them robust
and efficient for classifying food images. Experimental results on benchmarking
datasets show the superiority of our proposed method as compared to the
current-state-of-the-art methodologies even when trained with partially labeled
training data.
