This AI Paper Proposes a Novel Gradient-Based Method Called Cones to Analyze and Identify the Concept Neurons in Diffusion Models

Complex structure of the brain enables it to perform amazing cognitive and creative tasks. According to research, concept neurons in the human medial temporal lobe react differently to the semantic characteristics of the given stimuli. These neurons believed to be the foundation of high-level intellect, store temporal and abstract connections among experience items across spatiotemporal gaps. It is thus intriguing to learn if contemporary deep neural networks accept a similar structure of idea neurons as one of the most successful artificial intelligence systems.

Do generative diffusion models specifically encode several subjects independently with their neurons to emulate the creative capacity of the human brain? Chinese researchers have addressed this query from the viewpoint of a subject-driven generation. According to the semantics of the input text prompt, they suggest locating a small cluster of neurons that are parameters in the attention layer of a pretrained text-to-image diffusion model, such that altering values of those neurons can create a matching topic in various contents. These neurons are identified as the idea neurons linked to the relevant subject in the diffusion models. Identifying them can help us learn more about the fundamental workings of deep diffusion networks and offer a fresh approach to subject-driven generation. The idea neurons known as Cones1 are analyzed and identified using a unique gradient-based approach proposed in this study. They use them as scaling-down parameters whose absolute value can more effectively create the supplied topic while conserving existing knowledge. This motive may induce a gradient-based criterion for determining whether a parameter is a concept neuron. After a few gradient calculations, they may use this criterion to locate all the concept neurons. The interpretability of those idea neurons is then examined from various angles.

They start by looking into how resistant idea neurons are to changes in their values. They use float32, float16, quaternary, and binary digital precision to optimize a concept-implanting loss on the concept neurons, closing those concept neurons directly without training. Since binary digital accuracy takes the least storage space and requires no additional training, they utilize it as their default technique for subject-driven creation. The outcomes indicate consistent performance across all situations, showing neurons’ high robustness in managing the target topic. Concatenating idea neurons from different subjects can produce them all in the findings using this approach, which also allows for exciting additivity. This discovery of a straightforward but powerful affine semantic structure in the diffusion model parameter space may be a first. Additional fine-tuning based on concatenating can advance the multi-concept generating capacity to a new milestone: they are the first in a subject-driven generation to successfully produce four distinct, disparate subjects in a single image.

Eventually, neurons can be effectively employed in large-scale applications because of their sparsity and resilience. Many investigations on various categories, including human portraits, settings, decorations, etc., show that the approach is superior in interpretability and can generate several concepts. Comparing current subjectdriven approaches, storing the data necessary to develop a specific subject uses just around 10% of memory, making it incredibly cost-effective and environmentally friendly for use on mobile devices.

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