Layer Output

Layer output in deep neural networks is a crucial research area focusing on optimizing the information extracted at various network depths for improved model performance and robustness. Current research investigates strategies like retraining classifiers on features from all layers, augmenting last-layer features to enhance downstream task performance, and employing Bayesian methods to better quantify uncertainty in the output. These efforts aim to address issues such as spurious correlations, improve generalization, and enhance model efficiency, ultimately leading to more accurate and reliable machine learning models across diverse applications.