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How to Improve the Architecture of the Network to Create Better Results
For AI, we need data. Obviously because many solutions are data-driven, but we also need capable architectures. For instance, deep learning models, these convolutional or attention layers, it's like human brain. We need such architecture running on hardware, on edge devices, on mobile devices in many cases and they need to be efficient. So for that purpose, we have another four papers. The first one is X3KD. It does cross model and cross stage. This is the architecture stage and cross test, for instance, segmentation, detection. Second paper is EcoTTA. TTA here stands for test time adaptation. It's an efficient approach that improves continual test