  Convolutional neural networks (CNNs) and transformers, which are composed of
multiple processing layers and blocks to learn the representations of data with
multiple abstract levels, are the most successful machine learning models in
recent years. However, millions of parameters and many blocks make them
difficult to be trained, and sometimes several days or weeks are required to
find an ideal architecture or tune the parameters. Within this paper, we
propose a new algorithm for boosting Deep Convolutional Neural Networks
(BoostCNN) to combine the merits of dynamic feature selection and BoostCNN, and
another new family of algorithms combining boosting and transformers. To learn
these new models, we introduce subgrid selection and importance sampling
strategies and propose a set of algorithms to incorporate boosting weights into
a deep learning architecture based on a least squares objective function. These
algorithms not only reduce the required manual effort for finding an
appropriate network architecture but also result in superior performance and
lower running time. Experiments show that the proposed methods outperform
benchmarks on several fine-grained classification tasks.
