You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Whereas the above works invert a given image into the model’s latent space, we invert a user-provided concept. Moreover, we represent this concept as a new pseudo-word in the model’s vocabulary, allowing for more general and intuitive editing. To find these pseudo-words, we frame the task as one of inversion. We are given a fixed, pre-trained text-to-image model and a small (3-5) image set depicting the concept. We aim to find a single word embedding, such that sentences of the form “A photo of S_{*}” will lead to the reconstruction of images from our small set. This embedding is found through an optimization process, which we refer to as “Textual Inversion”.

Question: Whaat is the difference between inverting to the latent space and inverting to a concept? Aren't you inverting to a latent space anyways?
A concept, in this work, is represented by a set of multiple (3 to 5) images. The authors' approach attempts to invert these images collectively, to identify one word, S*, that can represent the concept embodied in all these input images. This process of collectively inverting a concept through multiple images is clearly different from merely inverting one image into a latent space. Even if both processes involve identifying a representation in latent space, the inputs of the processes differ.