To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Continual learning (CL) is a learning paradigm that emulates the human capability of
learning and accumulating knowledge continually without forgetting the previously learned …

While generative models produce high-quality images of concepts learned from a large-
scale database, a user often wishes to synthesize instantiations of their own concepts (for …

Text-to-image models offer unprecedented freedom to guide creation through natural
language. Yet, it is unclear how such freedom can be exercised to generate images of …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

Parametric language models (LMs), which are trained on vast amounts of web data, exhibit
remarkable flexibility and capability. However, they still face practical challenges such as …

The recent large-scale generative modeling has attained unprecedented performance
especially in producing high-fidelity images driven by text prompts. Text inversion (TI) …

Large language models (LLMs) suffer from catastrophic forgetting during continual learning.
Conventional rehearsal-based methods rely on previous training data to retain the model's …

Conventional natural language process (NLP) generation models are trained offline with a
given dataset for a particular task, which is referred to as isolated learning. Research on …

Lifelong seq2seq language generation models are trained with multiple domains in a
lifelong learning manner, with data from each domain being observed in an online fashion. It …