From Noise to Nuance: Advances in Deep Generative Image Models

• URL: http://arxiv.org/abs/2412.09656v1
• Date: Thu, 12 Dec 2024 02:09:04 GMT
• Title: From Noise to Nuance: Advances in Deep Generative Image Models
• Authors: Benji Peng, Chia Xin Liang, Ziqian Bi, Ming Liu, Yichao Zhang, Tianyang Wang, Keyu Chen, Xinyuan Song, Pohsun Feng,
• Abstract summary: Deep learning-based image generation has undergone a paradigm shift since 2021. Recent developments in Stable Diffusion, DALL-E, and consistency models have redefined the capabilities and performance boundaries of image synthesis. We investigate how enhanced multi-modal understanding and zero-shot generation capabilities are reshaping practical applications across industries.
• Score: 8.802499769896192
• License:
• Abstract: Deep learning-based image generation has undergone a paradigm shift since 2021, marked by fundamental architectural breakthroughs and computational innovations. Through reviewing architectural innovations and empirical results, this paper analyzes the transition from traditional generative methods to advanced architectures, with focus on compute-efficient diffusion models and vision transformer architectures. We examine how recent developments in Stable Diffusion, DALL-E, and consistency models have redefined the capabilities and performance boundaries of image synthesis, while addressing persistent challenges in efficiency and quality. Our analysis focuses on the evolution of latent space representations, cross-attention mechanisms, and parameter-efficient training methodologies that enable accelerated inference under resource constraints. While more efficient training methods enable faster inference, advanced control mechanisms like ControlNet and regional attention systems have simultaneously improved generation precision and content customization. We investigate how enhanced multi-modal understanding and zero-shot generation capabilities are reshaping practical applications across industries. Our analysis demonstrates that despite remarkable advances in generation quality and computational efficiency, critical challenges remain in developing resource-conscious architectures and interpretable generation systems for industrial applications. The paper concludes by mapping promising research directions, including neural architecture optimization and explainable generation frameworks.

Related papers

• Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learning [13.076087281398813]
  The proposed method is better than the existing mainstream comparison models in terms of knowledge retention and inference accuracy. Experimental results show that the proposed method is better than the existing mainstream comparison models in terms of knowledge retention and inference accuracy.
  arXiv  Detail & Related papers  (2025-01-13T05:16:14Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Boosting CNN-based Handwriting Recognition Systems with Learnable Relaxation Labeling [48.78361527873024]
  We propose a novel approach to handwriting recognition that integrates the strengths of two distinct methodologies. We introduce a sparsification technique that accelerates the convergence of the algorithm and enhances the overall system's performance.
  arXiv  Detail & Related papers  (2024-09-09T15:12:28Z)
• Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology [2.368662284133926]
  Digital Twin technology creates virtual replicas of physical objects, processes, or systems by replicating their properties, data, and behaviors. Digital Twin technology has leveraged Graph forecasting techniques in large-scale complex sensor networks to enable accurate forecasting and simulation of diverse scenarios. We introduce a hybrid architecture that enhances the hypergraph representation learning backbone by incorporating fast adaptation to new patterns and memory-based retrieval of past knowledge.
  arXiv  Detail & Related papers  (2024-08-22T14:08:45Z)
• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• Interpretable learning of effective dynamics for multiscale systems [5.754251195342313]
  We propose a novel framework of Interpretable Learning Effective Dynamics (iLED) iLED offers comparable accuracy to state-of-theart recurrent neural network-based approaches. Our results show that the iLED framework can generate accurate predictions and obtain interpretable dynamics.
  arXiv  Detail & Related papers  (2023-09-11T20:29:38Z)
• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• Dynamically Grown Generative Adversarial Networks [111.43128389995341]
  We propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator.
  arXiv  Detail & Related papers  (2021-06-16T01:25:51Z)
• Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence [3.874729481138221]
  Moore's law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is the exploration of alternative brain-inspired computing architectures that aim at achieving the flexibility and computational efficiency of biological neural processing systems. We provide a comprehensive overview of the field, highlighting the different levels of granularity at which this paradigm shift is realized.
  arXiv  Detail & Related papers  (2021-06-02T16:51:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.