AI painting is one branch of AIGC, which has been in the spotlight and controversy, and was even dubbed the “AIGC Year” in 2022. With the popularity of AI painting, one of the core technologies behind it, the Diffusion Model, has also gained immense popularity in the field of image generation, and it seems to be starting to surpass GAN.
Comparison of Diffusion Models with Other Models
Diffusion model leverages the UNet in the field of image segmentation, with stable training loss, sufficient data, and excellent model performance. Compared to GAN, which requires adversarial training with a discriminator, or VAE, which requires variational posterior, the loss of the diffusion model is really simple. The diffusion model only needs to “mimic” a very simple forward process corresponding to the reverse process. This simple and efficient training also allows the diffusion model to perform very well in many tasks, even surpassing GAN.
Overall, the diffusion model field is currently flourishing, which is somewhat reminiscent of the early days of GAN. However, the current training techniques have allowed the diffusion model to directly cross the model tuning phase of GAN and can be directly used for downstream tasks. There are still some core theoretical issues in this field that need research, providing valuable research content for researchers, and many ideas can be sparked. Moreover, since this model is already effective, its integration with downstream tasks is just beginning, and there are many areas where one can quickly stake a claim. As future issues in the diffusion model are resolved, it will gradually dominate the field of deep generative models.
Recently, while chatting with a top conference paper expert who graduated from Tsinghua University, I was amazed by his background. This expert has published more than twenty top conference papers! (Definitely a top paper harvesting machine). Apart from being a reviewer for top conferences and journals like CVPR, ECCV, ICCV, AAAI, ACM, MM, IJCV, he is also a senior research scientist at a major company, and each of these descriptions is significant~
We also invited this top conference paper expert to discuss the principles of diffusion models and their applications in cross-modal synthesis, to see how he understands the currently popular diffusion models~
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Free Diffusion Essential Papers
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To efficiently conduct research, mastering methods and reasonably utilizing available resources is crucial! Based on my experiences with junior researchers and my own research growth, I’ve found that many people generally lack a systematic research knowledge system, making it difficult to write a qualified paper. Not to mention discovering good innovation points and ideas!
Research papers can actually be categorized into the following three types:
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Discovering a new method and applying it to a known problem (Old Problem New Method)
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Discovering a new problem and extending existing research to this problem (New Problem Old Method)
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Discovering a new problem and proposing a new method for its analysis and research (New Problem New Method)
In terms of difficulty, it ranks as follows: New Problem Old Method < Old Problem New Method < New Problem New Method.
Thus, the innovation points and ideas that everyone struggles with actually have methodologies; once mastered, it becomes much easier to find an idea~
To generate an idea, there are two treasures—models + algorithms
Models can be understood as parts of the system model, which can be extensions of existing problem models; this is called innovation. As long as what you do has not been studied or has been studied very little by others, it can be considered innovation.
Once you propose a model, you need to choose a suitable algorithm to solve the problems within the model. This could involve finding an appropriate algorithm and reasonably improving it based on your scenario, which is already a great idea.
For research beginners, taking the first step in research is crucial: determining a research direction, selecting a topic, finding innovation points, generating ideas, and writing papers. This path can have simple or complex models. Anyway, regardless of the model, guidance from experts makes it much easier.
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As a researcher busy with papers, I know everyone must need some resources. Therefore, I have carefully compiled a package of over 20GB of AI top conference papers! It includes the latest top conference papers, books, and citation writing guidance materials, all organized for you from literature reading to paper writing~
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