A college senior returning to classes this fall has spent nearly their entire undergraduate career under the shadow--or in the embrace--of generative AI. ChatGPT first launched in November 2022, when that student was a freshman. As a department chair at Washington University in St. Louis, I witnessed the chaos it unleashed on campus. Students weren't sure what AI could do, or which uses were appropriate. Faculty were blindsided by how effectively ChatGPT could write papers and do homework.

We address your concerns as follows. Comparison of settings in related work. We add DANN+EWC and DANN+GEM in Table 3. We will elaborate on continual/incremental learning literature in the revision. See the comparison of these settings in Table 1.

Semi-supervised semantic segmentation requires the model to effectively propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-pixel prediction task is the large intra-class variation, i.e., regions belonging to the same class may exhibit a very different appearance even in the same

KD achieves consistent improvements over text-only language models and vokenization models, on several downstream language understanding tasks including GLUE, SQuAD, and SW AG.

An essential question in this framework is how to optimize meta-parameters.

Lastly, we demonstrate the superiority of stochastic proximal methods compared to subgradient-based approaches via extensive experiments.