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Lanlan Liu

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2 papers

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AAAI Conference 2021 Conference Paper

Dynamically Grown Generative Adversarial Networks

Lanlan Liu
Yuting Zhang
Jia Deng
Stefano Soatto

Recent work introduced progressive network growing as a promising way to ease the training for large GANs, but the model design and architecture-growing strategy still remain under-explored and needs manual design for different image data. In this paper, we propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator. It enjoys the benefits of both eased training because of progressive growing and improved performance because of broader architecture design space. Experimental results demonstrate new state-of-the-art of image generation. Observations in the search procedure also provide constructive insights into the GAN model design such as generatordiscriminator balance and convolutional layer choices.

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AAAI Conference 2018 Conference Paper

Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-Offs by Selective Execution

Lanlan Liu
Jia Deng

We introduce Dynamic Deep Neural Networks (D2 NN), a new type of feed-forward deep neural network that allows selective execution. Given an input, only a subset of D2 NN neurons are executed, and the particular subset is determined by the D2 NN itself. By pruning unnecessary computation depending on input, D2 NNs provide a way to improve computational efﬁciency. To achieve dynamic selective execution, a D2 NN augments a feed-forward deep neural network (directed acyclic graph of differentiable modules) with controller modules. Each controller module is a sub-network whose output is a decision that controls whether other modules can execute. A D2 NN is trained end to end. Both regular and controller modules in a D2 NN are learnable and are jointly trained to optimize both accuracy and efﬁciency. Such training is achieved by integrating backpropagation with reinforcement learning. With extensive experiments of various D2 NN architectures on image classiﬁcation tasks, we demonstrate that D2 NNs are general and ﬂexible, and can effectively optimize accuracy-efﬁciency trade-offs.

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