  In this paper, we describe a novel architecture which can generate hilarious
captions for a given input image. The architecture is split into two halves,
i.e. image captioning and hilarious text conversion. The architecture starts
with a pre-trained CNN model, VGG16 in this implementation, and applies
attention LSTM on it to generate normal caption. These normal captions then are
fed forward to our hilarious text conversion transformer which converts this
text into something hilarious while maintaining the context of the input image.
The architecture can also be split into two halves and only the seq2seq
transformer can be used to generate hilarious caption by inputting a
sentence.This paper aims to help everyday user to be more lazy and hilarious at
the same time by generating captions using CATNet.
