Layer Aggregation

Layer aggregation in deep learning focuses on combining information from multiple layers of a neural network to improve model performance and address limitations of relying solely on top-layer outputs. Current research explores this technique across diverse applications, including graph neural networks (GNNs), image processing (e.g., keypoint estimation and deepfake detection), and natural language processing (e.g., comment classification and speech recognition), often employing transformers and attention mechanisms. These methods aim to leverage the complementary information encoded at different layers, leading to more robust and accurate results in various domains, ultimately advancing the capabilities of deep learning models.