Wen Li

TL;DR: A novel algorithm termed as Tensorized Multi-view Subspace Representation Learning is established, which models elegantly the complementary information among different views, reduces redundancy of subspace representations, and then improves the accuracy of subsequent tasks.

TL;DR: In this paper, the first Mobile AI challenge was introduced, where the target is to develop an end-to-end deep learning-based video super-resolution solutions that can achieve a real-time performance on mobile GPUs.

TL;DR: E2GAN as discussed by the authors formulated the GAN architecture search problem as a Markov decision process (MDP) for smoother architecture sampling, which enables a more effective RL-based search algorithm by targeting the potential global optimal architecture.

TL;DR: Li et al. as discussed by the authors proposed a scale-aware domain adaptive Faster R-CNN (SA-da-faster R-Cnn) model to tackle the domain shift on two levels: image-level and instance-level.

TL;DR: A new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search that exploits an off-policy GAN architecture search algorithm that makes efficient use of the samples generated by previous policies.