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Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks
We create phantom embeddings from a subset of homogenous samples and use these phantom embeddings to decrease the inter-class similarity of instances in their latent embedding space.
Directly Optimizing IoU for Bounding Box Localization
In the scope of work seen currently in this domain, IoU is approximated by using the Huber loss as a proxy but this indirect method does not leverage the IoU information and treats the bounding box as four independent, unrelated terms of regression.
A.I. and Data-Driven Mobility at Volkswagen Financial Services AG
Machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research.
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