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As for most adaptation methods, most of the parameter updates for the model representation and the classifier are derived from the source and not the target. However, target accuracy is the goal, so we argue for optimizing as much as possible on the target data.

Dent improves adversarial/robust accuracy (%) by more than 30 percent (relative) against AutoAttack on CIFAR-10 while preserving natural/clean accuracy. The static defenses alter training, while dent alters testing, and so this separation of concerns makes dent compatible with many existing models and defenses.

Dynamic receptive field scale is optimized according to the output at test time. We optimize receptive field scales and filter parameters to minimize the output entropy. This gives a modest refinement for training and testing at the same scale, and generalization improves for testing at different scales.

We compose free-form filters and structured Gaussian filters by convolution to define a more general family of semi-structured filters than can be learned by either alone. Our composition makes receptive field scale, aspect, and orientation differentiable in a low-dimensional parameterization for efficient end-to-end learning.

We represent objects by a single point at their bounding box center. Other properties, such as object size, dimension, 3D extent, orientation, and pose are then regressed directly from image features at the center location.

It is well known that the success of machine learning methods on visual recognition tasks is highly dependent on access to large labeled datasets. Unfortunately, performance often drops significantly when the model is presented with data from a new deployment domain which it did not see in training, a problem known as dataset shift. The VisDA challenge aims to test domain adaptation methods’ ability to transfer source knowledge and adapt it to novel target domains.

While performance is improving for segmentation models trained and evaluated on the same data source, there has yet been limited research exploring the applicability of these models to new related domains. We propose the first unsupervised domain adaptation method for transferring semantic segmentation FCNs across image domains.