35M documents and dumped at 2015-12-01.

For the other datasets, we elected a number of heterogeneous descriptors:

-   Soccer: , , and .

-   Brodatz: , , and .

-   UW: , , , and , as visual descriptors, and *word2vecSum*, *word2vecAvg*, *doc2vecWiki*, and *doc2vecApnews*, as textual descriptors.

For the deep networks used for CNN-based visual feature extraction, as well as in the textual feature extraction with doc2vec, we take advantage of pre-trained models. This practice, known as transfer learning, has been effective in many scenarios [@kornblith:2018:imagenetModelTransfer], and it is also particularly beneficial for datasets that are not large enough to generalize the training of such large architectures, as in our case. Because the problem requires prediction of flood images, we prioritize, in the selection of visual descriptors, datasets for pre-training that focus on images of scenes, aiming at better generality to the target problem.

We perform the same preprocessing steps for every textual descriptor: lower case conversion, digit and punctuation removal, and English stop word removal. For *BoW* and *2grams*, we also apply Porter stemming.

The *word2vecSum* descriptor produces, for any input document, a vector corresponding to the sum of the word embedding vectors [@mikolov:2013:word2vec] related to each term 