![]() Through ongoing education, our stylists stay up to date on the latest. We find that both approaches to incorporating depth signals improve the robustness and generalization of the baseline SSL methods, though the first approach (with depth-channel concatenation) is superior. Hair is our passion, and our passion shows on every client that walks out of our doors. We evaluate these two approaches on three different SSL methods-BYOL, SimSiam, and SwAV-using ImageNette (10 class subset of ImageNet) and ImageNet-100. Second, we use the depth signal to generate novel views from slightly different camera positions, thereby producing a 3D augmentation for contrastive learning. ![]() First, we evaluate contrastive learning using an RGB+depth input representation. Using a signal provided by a pretrained state-of-the-art RGB-to-depth model (the Depth Prediction Transformer, Ranftl et al., 2021), we explore two distinct approaches to incorporating depth signals into the SSL framework. These augmentations ignore the fact that biological vision takes place in an immersive three-dimensional, temporally contiguous environment, and that low-level biological vision relies heavily on depth cues. Most SSL methods rely on augmentations obtained by transforming the 2D image pixel map. ![]() The Third Dimension is where energy congeals into a dark, dense pool of matter. Abstract: Self-Supervised Learning (SSL) methods operate on unlabeled data to learn robust representations useful for downstream tasks. The Third Dimension - Physical Reality of the Conscious Being. ![]()
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