This AI paper proposes a new gradient-based method called cones to analyze and identify conceptual neurons in diffusion models.

The complex structure of the brain allows it to perform amazing cognitive and creative tasks. According to the research, conceptual neurons in the human medial temporal lobe react differently to the semantic features of given stimuli. These neurons, believed to be the basis of high-level intellect, store abstract, temporal connections between elements of experience through spatiotemporal gaps. Therefore, it is intriguing whether contemporary deep neural networks accept a similar structure of ideation neurons as one of the most successful artificial intelligence systems.

Do generative diffusion models specifically encode multiple subjects independently with their neurons to emulate the creative capacity of the human brain? Chinese researchers have approached this question from the point of view of a subject-driven generation. Consistent with the semantics of the input text flag, they suggest placing a small group of neurons that are parameters in the attention layer of a pretrained text-to-image diffusion model, such that altering the values ​​of those neurons can create a Matching theme across multiple content. These neurons are identified as the idea neurons linked to the relevant topic in the diffusion models. Identifying them can help us learn more about the fundamental workings of deep broadcast networks and offer a new approach to subject-driven generation. Idea neurons known as Cones1 are analyzed and identified using a unique gradient-based approach proposed in this study. They use them as reduction parameters whose absolute value can more effectively create the given theme while preserving existing knowledge. This motif can induce a gradient-based criterion to determine if a parameter is a conceptual neuron. After some gradient calculations, they can use this criterion to locate all the conceptual neurons. The interpretability of these idea neurons is then examined from various angles.

They begin by investigating how resistant idea neurons are to changes in their values. They use digital float32, float16, quaternary, and binary precision to optimize a loss of concept implantation in concept neurons, shutting down those concept neurons directly without training. Since binary digital precision takes up the least storage space and requires no additional training, they use it as their default technique for theme-based creation. The results indicate consistent performance in all situations, demonstrating the high robustness of the neurons in managing the target subject. Concatenation of idea neurons from different subjects can produce them all in findings using this approach, which also allows for exciting additivity. This discovery of a simple but powerful cognate semantic structure in the parameter space of the diffusion model may be a novelty. Additional concatenation-based fine tuning can advance multi-concept generation capability to a new milestone: they are the first in a subject-based generation to successfully produce four distinct and disparate subjects in a single image.

Eventually, neurons can be effectively employed in large-scale applications due to their scarcity and resilience. Lots of research on various categories, including human portraits, scenery, decorations, etc., show that the approach is superior in interpretability and can generate various concepts. When comparing current theme-based approaches, storing the data required to develop a specific theme uses only about 10% of memory, making it incredibly cost-effective and environmentally friendly for use on mobile devices. .

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