15 citations · 16 across the 3 of their papers we have counts for
4 papers
Dynamically Grown Generative Adversarial Networks
Lanlan Liu, Yuting Zhang, Jia Deng +1
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…
A Unified Framework of Surrogate Loss by Refactoring and Interpolation
Lanlan Liu, Mingzhe Wang, Jia Deng
We introduce UniLoss, a unified framework to generate surrogate losses for training deep networks with gradient descent, reducing the amount of manual design of task-specific surro…
Generative Modeling for Small-Data Object Detection
Lanlan Liu, Michael Muelly, Jia Deng +2
This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This…
Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution
Lanlan Liu, Jia Deng
We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward deep neural network that allows selective execution. Given an input, only a subset of D2NN neurons are…