Encoding Layer

Encoding layers are fundamental components of neural networks, transforming input data into a lower-dimensional representation that captures essential features. Current research focuses on optimizing these layers for various applications, exploring techniques like autoencoders, Siamese networks, and transformer architectures to improve efficiency and accuracy, particularly in large language models and image compression. This work is crucial for advancing deep learning capabilities, addressing challenges such as computational cost and improving the performance of models across diverse datasets and architectures. The development of efficient and effective encoding layers directly impacts the scalability and applicability of numerous AI systems.