PaddingFlow: Improving Normalizing Flows with Padding-Dimensional Noise

Normalizing flow is a generative modeling approach with efficient sampling. However, Flow-based models suffer two issues, which are manifold and discrete data. If the target distribution is a manifold, which means the dimension of the latent target distribution and the dimension of the data distribution are unmatched, flow-based models might perform badly. Discrete data makes flow-based models collapse into a degenerate mixture of point masses. In this paper, to sidestep such two issues we propose PaddingFlow, a novel dequantization method, which improves normalizing flows with padding-dimensional noise. PaddingFlow is easy to implement, computationally cheap, widely suitable for various tasks, and generates samples that are unbiased estimations of the data. Especially, our method can overcome the limitation of existing dequantization methods that have to change the data distribution, which might degrade performance. We validate our method on the main benchmarks of unconditional density estimation, including five tabular datasets and four image datasets for VAE models, and the IK experiments which are conditional density estimation. The results show that PaddingFlow can provide improvement on all tasks in this paper.

PPNet: A Novel Neural Network Structure for End-to-End Near-Optimal Path Planning

The classical path planners, such as sampling-based path planners, have the limitations of sensitivity to the initial solution and slow convergence to the optimal solution. However, finding a near-optimal solution in a short period is challenging in many applications such as the autonomous vehicle with limited power/fuel. To achieve an end-to-end near-optimal path planner, we first divide the path planning problem into two subproblems, which are path's space segmentation and waypoints generation in the given path's space. We further propose a two-level cascade neural network named Path Planning Network (PPNet) to solve the path planning problem by solving the abovementioned subproblems. Moreover, we propose a novel efficient data generation method for path planning named EDaGe-PP. The results show the total computation time is less than 1/33 and the success rate of PPNet trained by the dataset that is generated by EDaGe-PP is about $2 \times$ compared to other methods. We validate PPNet against state-of-the-art path planning methods. The results show PPNet can find a near-optimal solution in 15.3ms, which is much shorter than the state-of-the-art path planners.