A Decade of Deep Learning: A Survey on The Magnificent Seven

TL;DR

This survey identifies seven influential deep learning algorithms from the past decade—ResNets, Transformers, GANs, VAEs, GNNs, CLIP, and Diffusion Models—and distills their architectures, mathematical foundations, training dynamics, extensions, applications, and challenges into a cohesive practical guide. It combines a broad expert survey with citation-based ranking to justify the selection and provides detailed, section-by-section analyses of each method, including core equations and training considerations. Beyond technical exposition, the paper discusses building blocks, optimization, normalization, and scheduling, connecting these components to each algorithm to aid deployment and adaptation. The work aims to assist both newcomers entering cutting-edge DL and seasoned researchers transitioning into this rapidly evolving field, offering a structured, application-oriented manual with guidance on future directions and open challenges.

Abstract

Deep learning has fundamentally reshaped the landscape of artificial intelligence over the past decade, enabling remarkable achievements across diverse domains. At the heart of these developments lie multi-layered neural network architectures that excel at automatic feature extraction, leading to significant improvements in machine learning tasks. To demystify these advances and offer accessible guidance, we present a comprehensive overview of the most influential deep learning algorithms selected through a broad-based survey of the field. Our discussion centers on pivotal architectures, including Residual Networks, Transformers, Generative Adversarial Networks, Variational Autoencoders, Graph Neural Networks, Contrastive Language-Image Pre-training, and Diffusion models. We detail their historical context, highlight their mathematical foundations and algorithmic principles, and examine subsequent variants, extensions, and practical considerations such as training methodologies, normalization techniques, and learning rate schedules. Beyond historical and technical insights, we also address their applications, challenges, and potential research directions. This survey aims to serve as a practical manual for both newcomers seeking an entry point into cutting-edge deep learning methods and experienced researchers transitioning into this rapidly evolving domain.

Figures (9)

• Figure 1: Examples of the questions provided to respondents, along with answers. The answers to the questions above have been randomly generated by the authors. For the question "Which Deep Learning algorithm are more contributing in current decade?" we suggested respondents to provide at least 5 algorithm examples.
• Figure 2: Distribution of respondents in our survey questionnaire.
• Figure 3: The ResNet architecture uses residual blocks with identity mappings, enabling deeper networks by addressing vanishing gradients and allowing efficient feature learning through shortcut connections.
• Figure 4: Illustration of the transformer architecture with encoder and decoder: the encoder processes input sequences, while the decoder generates outputs using self-attention, cross-attention, and feedforward layers.
• Figure 5: The GANs core architecture involves two neural networks: a generator creating fake data, and a discriminator distinguishing real from fake, trained adversarially.