The proposed approach is particularly useful for large-scale multidimensional grid data (e.g., 3D tomography), and for tasks that require context over a large receptive field (e.g., predicting the medical condition of entire organs). Our implicit representation of the tensor in the network enables processing large grids that could not be otherwise tractable in their uncompressed form. It then copies the data from the existing repository, and checks out a new set of working. Internally, git clone first calls git init to create a new repository. git clone is used to create a copy of an existing repository. The required number of samples grows only logarithmically with the size of the input. At a high level, they can both be used to 'initialize a new git repository.' However, git clone is dependent on git init. Instead, it actively selects local representative samples that we fetch out-of-core and on demand. CA is an adaptive sampling algorithm that is native to tensor decompositions and avoids working with the full high-resolution data explicitly. Our method combines a neural network encoder with a tensor train decomposition to learn a low-rank latent encoding, coupled with cross-approximation (CA) to learn the representation through a subset of the original samples. We propose an end-to-end trainable framework that processes large-scale visual data tensors by looking at a fraction of their entries only.
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