Dec 9, 2024 06:10 PM
https://www.eurekalert.org/news-releases/1067536
INTRO: The capabilities of large-scale pre-trained AI models have recently skyrocketed, as demonstrated by large-scale vision-language models like CLIP or ChatGPT. These typical generalist models can perform reasonably well in tasks covering a large variety of fields, which has paved the way for their widespread adoption by the public. However, such versatility no doubt comes at a cost.
Training and operating large-scale models consume extreme amounts of energy and time, which goes against sustainability goals and limits the types of computers they can be deployed on. Moreover, in many practical applications, people want AI models to fulfil specific roles rather than be jacks-of-all-trades. In such cases, a model’s generalist capabilities might be useless and even counter-productive, reducing accuracy. Could there be a way to leverage large-scale pre-trained models more efficiently by having them ‘forget’ unnecessary information?
In a recent paper that will be presented in Neural Information Processing Systems (NeurIPS 2024), a research team led by Associate Professor Go Irie from Tokyo University of Science (TUS), Japan, sought to tackle this problem. They developed a methodology dubbed “black-box forgetting,” by which one can iteratively optimize the text prompts presented to a black-box vision-language classifier model to have it selectively ‘forget’ some of the classes it can recognize. Co-authors of this study included Mr. Yusuke Kuwana and Mr. Yuta Goto, both from TUS, as well as Dr. Takashi Shibata from NEC Corporation.
“In practical applications, the classification of all kinds of object classes is rarely required. For example, in an autonomous driving system, it would be sufficient to recognize limited classes of objects such as cars, pedestrians, and traffic signs. We would not need to recognize food, furniture, or animal species,” explains Dr. Irie, “Retaining the classes that do not need to be recognized may decrease overall classification accuracy, as well as cause operational disadvantages such as the waste of computational resources and the risk of information leakage.”
Although some methods for selective forgetting in pre-trained models do exist, these assume a white-box setting, where the user has access to the internal parameters and architecture of the model. More often than not, users deal with black-boxes; they do not have access to the model itself or most of its information due to commercial or ethical reasons. Thus, the researchers had to employ a so-called derivative-free optimization strategy—one that does not require access to the model’s gradients.
To this end, they extended a method known as CMA-ES, with the image classifier model CLIP as the target model for this study... (MORE - details, no ads)
INTRO: The capabilities of large-scale pre-trained AI models have recently skyrocketed, as demonstrated by large-scale vision-language models like CLIP or ChatGPT. These typical generalist models can perform reasonably well in tasks covering a large variety of fields, which has paved the way for their widespread adoption by the public. However, such versatility no doubt comes at a cost.
Training and operating large-scale models consume extreme amounts of energy and time, which goes against sustainability goals and limits the types of computers they can be deployed on. Moreover, in many practical applications, people want AI models to fulfil specific roles rather than be jacks-of-all-trades. In such cases, a model’s generalist capabilities might be useless and even counter-productive, reducing accuracy. Could there be a way to leverage large-scale pre-trained models more efficiently by having them ‘forget’ unnecessary information?
In a recent paper that will be presented in Neural Information Processing Systems (NeurIPS 2024), a research team led by Associate Professor Go Irie from Tokyo University of Science (TUS), Japan, sought to tackle this problem. They developed a methodology dubbed “black-box forgetting,” by which one can iteratively optimize the text prompts presented to a black-box vision-language classifier model to have it selectively ‘forget’ some of the classes it can recognize. Co-authors of this study included Mr. Yusuke Kuwana and Mr. Yuta Goto, both from TUS, as well as Dr. Takashi Shibata from NEC Corporation.
“In practical applications, the classification of all kinds of object classes is rarely required. For example, in an autonomous driving system, it would be sufficient to recognize limited classes of objects such as cars, pedestrians, and traffic signs. We would not need to recognize food, furniture, or animal species,” explains Dr. Irie, “Retaining the classes that do not need to be recognized may decrease overall classification accuracy, as well as cause operational disadvantages such as the waste of computational resources and the risk of information leakage.”
Although some methods for selective forgetting in pre-trained models do exist, these assume a white-box setting, where the user has access to the internal parameters and architecture of the model. More often than not, users deal with black-boxes; they do not have access to the model itself or most of its information due to commercial or ethical reasons. Thus, the researchers had to employ a so-called derivative-free optimization strategy—one that does not require access to the model’s gradients.
To this end, they extended a method known as CMA-ES, with the image classifier model CLIP as the target model for this study... (MORE - details, no ads)