A new study published in Nature Communications found that it is impossible to draw a direct causal link between a specific artwork and the output of diffusion-based image generators like Midjourney an…
A study from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) found that AI image generators trained on large datasets exhibit 'attribution decay,' where individual training image…
A new study from MIT researchers Zheng Dai and David Gifford, published in Nature, finds that the more data a generative AI model is trained on, the harder it is to attribute its output to any single …
A new MIT CSAIL study published in Nature Communications finds that individual training examples often have no measurable influence on any specific output of large generative image models, a phenomeno…
A peer-reviewed MIT CSAIL study published August 18 in Nature Communications found that diffusion-model outputs become harder to attribute to any single training image, person, or artist as training s…