  In this paper, we examine the use of Transfer Learning using Pretrained Audio
Neural Networks (PANNs), and propose an architecture that is able to better
leverage the acoustic features provided by PANNs for the Automated Audio
Captioning Task. We also introduce a novel self-supervised objective,
Reconstruction Latent Space Similarity Regularization (RLSSR). The RLSSR module
supplements the training of the model by minimizing the similarity between the
encoder and decoder embedding. The combination of both methods allows us to
surpass state of the art results by a significant margin on the Clotho dataset
across several metrics and benchmarks.
