One20 major reason isthat the filter bank isredundant and contains enough representation power. When21 sampled with different random seeds, the estimator is capable of generating abundant kernels.22 However,wealsoobservetheoscillations ofaccuracy(about 0.5% Top-1accuracyvariance in1023 runs) when the small portion of DoG filters are removed. We perform object detection experiments on COCO 2017 [8] dataset, which41 contains 118K images for training, 5K images for validation, and 20K images for test-dev. We also evaluate our method on semantic segmentation, utilizing the50 widely-used ADE20K [12] dataset. ADE20K covers 150 semantic classes, with 20K images for51 training, 2K images for testing, and 3K for testing.

Generalization to new environments remains a major challenge in deep reinforcement learning (RL). Current methods fail to generalize to unseen environments even when trained on similar settings [19, 51, 71, 11, 21, 12, 60].

However, deploying a room-layout model using 360-images in a new target domain remains a challenging problem.