“This work introduces a new neural network architecture that uses bidirectional associations-based pooling to extract high-level features and labels from multi-label data. Unlike the pooling approaches reported in the literature, our proposal does not require input data to have any topological properties as typically occurs with images and videos. The numerical results show that our bidirectional pooling helps reduce the number of problem features and labels while preserving the discriminatory power of the network.”