We evaluate Okapi using three datasets - iWildCam, PovertyMap, and CivilComments - taken from the WILDS 2.0 benchmark [63]. These datasets were chosen specifically due to the poor performance reported by [63] for semi-supervised and domain adaptation methods across the board, in relation to the ERM baselines. For PovertyMap in particular, ERM was found to vastly outperform any competing methods utilising the unlabelled data and/or domain labels. The task is multiclass species classification of animals in camera trap images. The dataset contains 1022K images of animals annotated with the domain, s, that identifies the camera trap that captured it.

Perceiving the pre-eminence of image reconstruction in representation learning, we propose SMG (Separated Models for Generalization), a novel approach that exploits image reconstruction for generalization.

Neural Information Processing Systems http://nips.cc/

Recent work has shown that CNN-based depth and ego-motion estimators can be learned using unlabelled monocular videos. However, the performance is limited by unidentified moving objects that violate the underlying static scene assumption in geometric image reconstruction.