: Image alignment example -We iteratively find the image transformation parameters that produce the downsampled target image when applied to the source image using different sampling strategies. We visualize the transformed source image at convergence when using bilinear sampling and the proposed linearized sampling. We further display the optimization path -iterations -for each method. The proposed linearized sampling provides improved gradients, leading to better convergence. We propose a novel image sampling method for differentiable image transformation in deep neural networks. The sampling schemes currently used in deep learning, such as Spatial Transformer Networks, rely on bilinear interpolation, which performs poorly under severe scale changes, and more importantly, results in poor gradient propagation. This is due to their strict reliance on direct neighbors. Instead, we propose to generate random auxiliary samples in the vicinity of each pixel in the sampled image, and create a linear approximation with their intensity values. We then use this approximation as a differentiable formula for the transformed image. We demonstrate that our approach produces more representative gradients with a wider basin of convergence for image alignment, which leads to considerable performance improvements when training networks for classification tasks. This is not only true under large downsampling, but also when there are no scale changes. We compare our approach with multi-scale sampling and show that we outperform it. We then demonstrate that our improvements to the sampler are compatible with other tangential improvements to Spatial Transformer Networks and that it further improves their performance. 1 1 Code and models are available at https://github.com/ vcg-uvic/linearized_multisampling_release.
Jiang et al. REF demonstrate the poor gradient propagation of the commonly used Bilinear sampling.
Linearized Multi-Sampling for Differentiable Image Transformation
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Computer Science