To address these issues, we introduce a novel Bayesian meta-learning approach called meta-variational dropout (MetaVD).

The subtext to each question is initalics.Theanswers are in plain text with no formatting. Was there a specific gap that needed to be filled?Please provide a description.The Dollar Street dataset is a supervised image dataset derived from Gapminder'sDollar Street project (https://www.gapminder.org/dollar-street)that It was created with three goals in mind:1. Make available a highly curated set of images with valuable metadata (e.g.country, monthly income) that is more closely representative of the geographicand socioeconomic diversity of the world when compared with existing imagedatasets.2. Help combat bias in downstream applications (e.g.

Work todate has highlighted the challenges inmitigating bias within these datasets.

In this document, we provide details and supplementary materials that cannot fit into the main manuscript due to the page limit. The specific form ofcenter distribution isunknown, but we can still train a generatorG to approximate it. IfR(G,D,T)),wechooseλ=0, i.e., no restriction onR(G,D,T)), to obtain the minimal cost. IfR(G,D,T)) >, then a large λshould be applied as apenalization. According to the derivation of Eq. (3), we obtain arelaxed versionoftheintractableEq.(2),expressedasfollows: min Inknowledge distillation, student models arecrafted using unlabeled datasets, where only thesoft targets from teachers are utilized.

Continual learning requires the model to learn multiple tasks in a sequential order.