AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes a general method for learning task and scale based attention representations in Multi-Task Learning (MTL) for vision that adds an attention modulated feature as residual information to the processing of each scale stage within the model, including the final layer of task outputs.
Extensive experimental results show that training on the automatically generated optical flow outperforms methods that are trained on rigid synthetic data using FlowNet-S, LiteFlowNet, PWC-Net, PWC-Net, and RAFT.
It is concluded that adversarial training is beneficial if and only if the reconstruction loss is not too constrained, and non-adversarial training outperforms (or is on par with) any method trained with a GAN when a constrained reconstruction loss is used in combination with batch normalisation.
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