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TL;DL The first paper integrates Neural Architecture Search (NAS) with instance awareness and searches for a distribution of neural architectures to further expoit the power of NAS. InstaNAS achieves significant improvement in accuracy-latency tradeoff than single model. Our qualitative results show the controller policy shares many interesting similarities to human perception.

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• Project page (with interactive demo): https://hubert0527.github.io/InstaNAS
• Paper: https://arxiv.org/abs/1811.10201
• We will release our code soon here: https://github.com/AnjieZheng/InstaNas

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Also see our other recent works:

• COCO-GAN: Generation by Parts via Conditional Coordinating:TL;DR We show that it is possible to generate images by parts with a conditional coordinate mechanism. Our model preserves the state-of-the-art FID score and provides multiple interesting applications.Project Page: https://hubert0527.github.io/COCO-GAN/
• Point-to-point Video Generation:TL;DR We propose a new paradigm for controllable video generation between a pair of start- and end-frame. We show that our approach can generate various lengths of videos without loss of quality and diversity. We also showcase several new extensions with the proposed framework.Project Page: https://zswang666.github.io/P2PVG-Project-Page/
• Conditional Cost-Volume Normalization for Sparse Sensory Data:TL;DR We investigate and propose a novel normalization module for fusing sparse sensory data (3D LiDAR) and dense imagery data (stereo image). We show that our method effectively utilizes sparse sensory and brings significant performance, robustness and sensitivity improvement.Project Page: https://zswang666.github.io/Stereo-LiDAR-CCVNorm-Project-Page/