[City, State] – [Date] – Just days after his last groundbreaking work, He Kaiming, a leading figure in the field of artificial intelligence, has released a new paper introducing a novel generative model. The research team, drawing inspiration from the self-similar structures found in mathematical fractals, has developed what they call Fractal Generative Models (FGMs).
The core concept behind FGMs lies in modularity, a principle well-established in computer science. Just as deep neural networks are built from atomic layers as modular units, modern generative models like diffusion models and autoregressive models are constructed from atomic generation steps, each implemented by a deep neural network. This modular approach allows for the creation of complex systems by combining these building blocks.
The research team, led by He Kaiming, proposes abstracting the generative model itself as a module to develop more advanced generative models. The paper’s first author is Tianhong Li, a postdoctoral researcher at MIT, and the second author is Qinyi Sun, an undergraduate student (junior) at MIT.
The research paper, titled Fractal Generative Models, can be found at: https://arxiv.org/pdf/2502.17437v1
The corresponding GitHub repository is available at: https://github.com/LTH14/fractalgen
Specifically, the Fractal Generative Model constructs itself by recursively calling a generative model of the same kind within its architecture. This recursive strategy yields a generative framework, as illustrated in Figure 1 of the paper.
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Implications and Future Directions
While details are currently limited, the potential implications of Fractal Generative Models are significant. The reported 4000x increase in efficiency suggests a major breakthrough in the field of image generation, potentially allowing for the creation of high-resolution images with significantly reduced computational resources. Further research will be needed to fully understand the capabilities and limitations of FGMs, as well as their potential applications in various domains.
References:
- Li, T., Sun, Q., & He, K. (2025). Fractal Generative Models. arXiv preprint arXiv:2502.17437.
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