adaptation

Our method, Diffusion-Driven Adaptation (DDA), adapts test inputs to enhance model accuracy on shifted data. Utilizing a generative diffusion model with image guidance and classifier self-ensembling, DDA surpasses traditional model adaptation approaches in handling various types of data corruption, diverse data quantities, and dependencies, as confirmed by the ImageNet-C benchmark tests.

We introduce AdaContrast, a novel approach to test-time adaptation that leverages contrastive learning and an advanced online pseudo labeling scheme. AdaContrast incorporates a memory queue for label refinement, resulting in improved performance, enhanced memory efficiency, and better model calibration. It outperforms existing methods and demonstrates reduced sensitivity to hyperparameters.

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.

Tent equips a model to adapt itself to new and different data during testing ☀️ 🌧 ❄️. Tented models adapt online and batch-by-batch to reduce error on dataset shifts like corruptions, simulation-to-real discrepancies, and other differences between training and testing data.

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.

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.