Implicit Filter Sparsification In Convolutional Neural Networks

We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained with adaptive gradient descent techniques and L2 regularization or weight decay. Through an extensive empirical study (Mehta et al., 2019) we hypothesize the mechanism behind the sparsification process, and find surprising links to certain filter sparsification heuristics proposed in literature. Emergence of, and the subsequent pruning of selective features is observed to be one of the contributing mechanisms, leading to feature sparsity at par or better than certain explicit sparsification / pruning approaches. In this workshop article we summarize our findings, and point out corollaries of selective-featurepenalization which could also be employed as heuristics for filter pruningComment: ODML-CDNNR 2019 (ICML'19 workshop) extended abstract of the CVPR 2019 paper "On Implicit Filter Level Sparsity in Convolutional Neural Networks, Mehta et al." (arXiv:1811.12495

Example-based learning for single-image super-resolution and JPEG artifact removal

This paper proposes a framework for single-image super-resolution and JPEG artifact removal. The underlying idea is to learn a map from input low-quality images (suitably preprocessed low-resolution or JPEG encoded images) to target high-quality images based on example pairs of input and output images. To retain the complexity of the resulting learning problem at a moderate level, a patch-based approach is taken such that kernel ridge regression (KRR) scans the input image with a small window (patch) and produces a patchvalued output for each output pixel location. These constitute a set of candidate images each of which reflects different local information. An image output is then obtained as a convex combination of candidates for each pixel based on estimated confidences of candidates. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as it has been done in existing example-based super-resolution algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing super-resolution and JPEG artifact removal methods shows the effectiveness of the proposed method. Furthermore, the proposed method is generic in that it has the potential to be applied to many other image enhancement applications

Cyclo­hexane-1,2-diammonium bis­(pyridine-2-carboxyl­ate)

In the dication of the title salt, C6H16N2 2+·2C6H4NO2 −, the two ammonium groups are in the equatorial positions of the chair-shaped cyclo­hexyl ring. In the crystal, the cations and anions are linked by N—H⋯O and N—H⋯N hydrogen bonds, forming a layer network parallel to the ac plane. Weak π–π inter­actions between adjacent pyridine rings with a centroid–centroid distance of 3.589 (2) Å are also present

Bis(2,2′-bipyridine-κ2 N,N′)dichlorido­platinum(IV) dichloride monohydrate

In the title complex, [PtCl2(C10H8N2)2]Cl2·H2O, the Pt4+ ion is six-coordinated in a distorted octa­hedral environment by four N atoms from the two 2,2′-bipyridine ligands and two Cl atoms. As a result of the different trans influences of the N and Cl atoms, the Pt—N bonds trans to the Cl atom are slightly longer than those trans to the N atom. The compound displays inter­molecular hydrogen bonding between the water mol­ecule and the Cl anions. There are inter­molecular π–π inter­actions between adjacent pyridine rings, with a centroid–centroid distance of 3.962 Å

BoIR: Box-Supervised Instance Representation for Multi-Person Pose Estimation

Single-stage multi-person human pose estimation (MPPE) methods have shown great performance improvements, but existing methods fail to disentangle features by individual instances under crowded scenes. In this paper, we propose a bounding box-level instance representation learning called BoIR, which simultaneously solves instance detection, instance disentanglement, and instance-keypoint association problems. Our new instance embedding loss provides a learning signal on the entire area of the image with bounding box annotations, achieving globally consistent and disentangled instance representation. Our method exploits multi-task learning of bottom-up keypoint estimation, bounding box regression, and contrastive instance embedding learning, without additional computational cost during inference. BoIR is effective for crowded scenes, outperforming state-of-the-art on COCO val (0.8 AP), COCO test-dev (0.5 AP), CrowdPose (4.9 AP), and OCHuman (3.5 AP). Code will be available at https://github.com/uyoung-jeong/BoIRComment: Accepted to BMVC 2023, 19 pages including the appendix, 6 figures, 7 table

Axial strain dependence of all-fiber acousto-optic tunable filters

We report the axial strain dependence of two types of all-fiber acousto-optic tunable filters based on flexural and torsional acoustic waves. Experimental observation of the resonant wavelength shift under applied axial strain could be explained by theoretical consideration of the combination of acoustic and optical effects. We discuss the possibility of suppressing the strain effect in the filters, or conversely, the possibility of using the strain dependence for wavelength tuning or strain sensors