A Novel Graphic Bending Transformation on Benchmark

• URL: http://arxiv.org/abs/2004.10042v2
• Date: Fri, 11 Dec 2020 12:37:40 GMT
• Title: A Novel Graphic Bending Transformation on Benchmark
• Authors: Chunxiuzi Liu and Fengyang Sun and Qingrui Ni and Lin Wang and Bo Yang
• Abstract summary: We investigate a novel graphic conformal mapping transformation on benchmark problems to deform the function shape. Experiments indicate the same spends more search budget and encounter more failures on the conformal bent functions than the rotated version.
• Score: 6.6326947833070395
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classical benchmark problems utilize multiple transformation techniques to increase optimization difficulty, e.g., shift for anti centering effect and rotation for anti dimension sensitivity. Despite testing the transformation invariance, however, such operations do not really change the landscape's "shape", but rather than change the "view point". For instance, after rotated, ill conditional problems are turned around in terms of orientation but still keep proportional components, which, to some extent, does not create much obstacle in optimization. In this paper, inspired from image processing, we investigate a novel graphic conformal mapping transformation on benchmark problems to deform the function shape. The bending operation does not alter the function basic properties, e.g., a unimodal function can almost maintain its unimodality after bent, but can modify the shape of interested area in the search space. Experiments indicate the same optimizer spends more search budget and encounter more failures on the conformal bent functions than the rotated version. Several parameters of the proposed function are also analyzed to reveal performance sensitivity of the evolutionary algorithms.

Related papers

• A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features [60.76577388438418]
  3D anomaly detection (AD) is a crucial task in computer vision, aiming to identify anomalous points or regions from point cloud data.<n>Existing methods may encounter challenges when handling point clouds with changes in orientation and position because the resulting features may vary significantly.<n>We propose a novel Rotationally Invariant Features (RIF) framework for 3D AD, which maps each point into a rotationally invariant space to maintain consistency of representation.
  arXiv  Detail & Related papers  (2025-11-17T08:16:05Z)
• Geometrically Constrained and Token-Based Probabilistic Spatial Transformers [5.437226012505534]
  We revisit Spatial Transformer Networks (STNs) as a canonicalization tool for transformer-based vision pipelines.<n>We propose a probabilistic, component-wise extension that improves robustness.<n> Experiments on challenging moth classification benchmarks demonstrate that our method consistently improves robustness compared to other STNs.
  arXiv  Detail & Related papers  (2025-09-14T11:30:53Z)
• Stabilizing Backpropagation Through Time to Learn Complex Physics [21.850601375335074]
  In physics simulations, backpropagating feedback is crucial to acquiring temporally coherent behavior. The alternative vector field we propose follows from two principles: physics simulators have a balanced gradient flow, and certain modifications to the backpropagation pass leave the positions of the original minima unchanged. Our final procedure is easily implementable via a sequence of gradient stopping and component-wise comparison operations.
  arXiv  Detail & Related papers  (2024-05-03T12:20:08Z)
• PRAGO: Differentiable Multi-View Pose Optimization From Objectness Detections [19.211193336526346]
  We propose a Pose-refined Rotation Averaging Graph Optimization (PRAGO) method for differentiable estimating camera poses from a set of images. Our method reconstructs the rotational pose, and in turn, the absolute pose, in a differentiable manner benefiting from the optimization of a sequence of geometrical tasks. We show that PRAGO is able to outperform non-differentiable solvers on small and sparse scenes extracted from 7-Scenes achieving a relative improvement of 21% for rotations while achieving similar translation estimates.
  arXiv  Detail & Related papers  (2024-03-13T14:42:55Z)
• FRED: Towards a Full Rotation-Equivariance in Aerial Image Object Detection [28.47314201641291]
  We introduce a Fully Rotation-Equivariant Oriented Object Detector (FRED) Our proposed method delivers comparable performance on DOTA-v1.0 and outperforms by 1.5 mAP on DOTA-v1.5, all while significantly reducing the model parameters to 16%.
  arXiv  Detail & Related papers  (2023-12-22T09:31:43Z)
• Learning Transformations To Reduce the Geometric Shift in Object Detection [60.20931827772482]
  We tackle geometric shifts emerging from variations in the image capture process. We introduce a self-training approach that learns a set of geometric transformations to minimize these shifts. We evaluate our method on two different shifts, i.e., a camera's field of view (FoV) change and a viewpoint change.
  arXiv  Detail & Related papers  (2023-01-13T11:55:30Z)
• Orthonormal Convolutions for the Rotation Based Iterative Gaussianization [64.44661342486434]
  This paper elaborates an extension of rotation-based iterative Gaussianization, RBIG, which makes image Gaussianization possible. In images its application has been restricted to small image patches or isolated pixels, because rotation in RBIG is based on principal or independent component analysis. We present the emphConvolutional RBIG: an extension that alleviates this issue by imposing that the rotation in RBIG is a convolution.
  arXiv  Detail & Related papers  (2022-06-08T12:56:34Z)
• Smooth over-parameterized solvers for non-smooth structured optimization [3.756550107432323]
  Non-smoothness encodes structural constraints on the solutions, such as sparsity, group sparsity, low-rank edges and sharp edges. We operate a non-weighted but smooth overparametrization of the underlying nonsmooth optimization problems. Our main contribution is to apply the Variable Projection (VarPro) which defines a new formulation by explicitly minimizing over part of the variables.
  arXiv  Detail & Related papers  (2022-05-03T09:23:07Z)
• Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence [100.6913091147422]
  Existing rotated object detectors are mostly inherited from the horizontal detection paradigm. In this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology.
  arXiv  Detail & Related papers  (2021-06-03T14:29:19Z)
• Efficient and Differentiable Shadow Computation for Inverse Problems [64.70468076488419]
  Differentiable geometric computation has received increasing interest for image-based inverse problems. We propose an efficient yet efficient approach for differentiable visibility and soft shadow computation. As our formulation is differentiable, it can be used to solve inverse problems such as texture, illumination, rigid pose, and deformation recovery from images.
  arXiv  Detail & Related papers  (2021-04-01T09:29:05Z)
• GAN "Steerability" without optimization [32.63317794951011]
  "steering" directions correspond to semantically meaningful image transformations. We show that "steering" trajectories can be computed in closed form directly from the generator's weights.
  arXiv  Detail & Related papers  (2020-12-09T21:34:34Z)
• Aligning Partially Overlapping Point Sets: an Inner Approximation Algorithm [80.15123031136564]
  We propose a robust method to align point sets where there is no prior information about the value of the transformation. Our algorithm does not need regularization on transformation, and thus can handle the situation where there is no prior information about the values of the transformations. Experimental results demonstrate the better robustness of the proposed method over state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-07-05T15:23:33Z)
• A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding [57.1077544780653]
  We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems. We focus on convex games, solved by local agents represented by the nodes of a graph and interacting through regularization functions. This approach is appealing for solving imaging problems, as it allows the use of classical image priors within deep models that are trainable end to end.
  arXiv  Detail & Related papers  (2020-06-26T08:34:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.