  Although neural networks are conventionally optimized towards zero training
loss, it has been recently learned that targeting a non-zero training loss
threshold, referred to as a flood level, often enables better test time
generalization. Current approaches, however, apply the same constant flood
level to all training samples, which inherently assumes all the samples have
the same difficulty. We present AdaFlood, a novel flood regularization method
that adapts the flood level of each training sample according to the difficulty
of the sample. Intuitively, since training samples are not equal in difficulty,
the target training loss should be conditioned on the instance. Experiments on
datasets covering four diverse input modalities - text, images, asynchronous
event sequences, and tabular - demonstrate the versatility of AdaFlood across
data domains and noise levels.
