Adding synthetic bad weather made our real-weather sky accuracy worse on every architecture — the cause was a geometry missing from the training set
We were fine-tuning on physically-modelled dust, night, fog, lens rain and lens mud. On synthetic held-out corruptions (ImageNet-C fog/spatter/motion blur, an unprocessing-based low-light model — generators never used to make training data) it worked. On real adverse weather, drivable-surface IoU i…
Read the full story at r/deeplearning ↗