Deep Learning Pipeline

Deep learning pipelines integrate multiple neural network models to perform complex tasks, aiming to improve efficiency and accuracy beyond what single models can achieve. Current research emphasizes optimizing various pipeline stages, including data preprocessing, feature extraction (often using convolutional neural networks, Siamese networks, or transformers), and model training, with a focus on addressing challenges like data scarcity, computational cost, and robustness to out-of-distribution data. These pipelines are impacting diverse fields, enabling advancements in medical image analysis (e.g., lesion tracking, cancer diagnosis), environmental monitoring (e.g., fire prediction, biodiversity assessment), and other applications requiring sophisticated data processing and pattern recognition. The development of efficient and robust pipelines is a key area of ongoing research, with a growing focus on energy efficiency and trustworthiness of the resulting models.