Enhancing Deep Learning: Leveraging Skip Connections and Memory Efficiency

With the introduction of skip connections, deep neural network models can now be substantially deeper and can be trained more efficiently. This paper takes Dense Convolutional Network (DenseNet) [5], a cutting-edge architecture, highlighting the significant benefits of establishing dense interconnections between all layers in a unidirectional manner. The paper also discusses all the compelling advantages of DenseNet and evaluates the performance of its architecture on a Kaggle dataset (Dog-Breed Identification) [7]. Furthermore, it explores techniques for optimizing memory utilization while engaged in the training process. This research sheds light on how skip connections have contributed to the efficiency and effectiveness of deep learning models.