We introduce a probability distribution, combined with an efficient sampling algorithm, for weights and biases of fully-connected neural networks.

In this paper we tackle the problem of Generalized Category Discovery (GCD).

Medical Expenditure Dataset (MEPS4): This is used to predict whether a person has high utilizationornot. Fortabular datasets, weuse the batch size of 64, and train the model for a maximum of 20 epochs. Adversarial Training (Adversarial): Consider that the classification model isf(x) = c(g(x)) where g(x) is the encoder andc() is the classification head. For adversarial training, another adversarial classifiercadv()is also constructed. The classification headc()and the adversarial classifier cadv() are trained simultaneously.

Toaddress low-resource settings withnoaccess tosensitive attribute annotations, we leverage a bias-amplified model to generate proxy annotations for sensitive attributes.

We tackle the problem ofnovel class discovery and localization (NCDL). In this setting, we assume a source dataset with supervision for only some object classes.