Robust and Generalizable Visual Representation Learning via Random Convolutions

• URL: http://arxiv.org/abs/2007.13003v3
• Date: Mon, 3 May 2021 16:12:15 GMT
• Title: Robust and Generalizable Visual Representation Learning via Random Convolutions
• Authors: Zhenlin Xu, Deyi Liu, Junlin Yang, Colin Raffel, Marc Niethammer
• Abstract summary: We show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation. Our method can benefit downstream tasks by providing a more robust pretrained visual representation.
• Score: 44.62476686073595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation. Random convolutions are approximately shape-preserving and may distort local textures. Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local textures. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training. When applying a network trained with our approach to unseen domains, our method consistently improves the performance on domain generalization benchmarks and is scalable to ImageNet. In particular, in the challenging scenario of generalizing to the sketch domain in PACS and to ImageNet-Sketch, our method outperforms state-of-art methods by a large margin. More interestingly, our method can benefit downstream tasks by providing a more robust pretrained visual representation.

Related papers

• CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition [73.51329037954866]
  We propose a robust global representation method with cross-image correlation awareness for visual place recognition. Our method uses the attention mechanism to correlate multiple images within a batch. Our method outperforms state-of-the-art methods by a large margin with significantly less training time.
  arXiv  Detail & Related papers  (2024-02-29T15:05:11Z)
• Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks [53.67497327319569]
  We introduce a novel neural rendering technique to solve image-to-3D from a single view. Our approach employs the signed distance function as the surface representation and incorporates generalizable priors through geometry-encoding volumes and HyperNetworks. Our experiments show the advantages of our proposed approach with consistent results and rapid generation.
  arXiv  Detail & Related papers  (2023-12-24T08:42:37Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Self-Supervised Single-Image Deconvolution with Siamese Neural Networks [6.138671548064356]
  Inverse problems in image reconstruction are fundamentally complicated by unknown noise properties. Deep learning methods allow for flexible parametrization of the noise and learning its properties directly from the data. We tackle this problem with Fast Fourier Transform convolutions that provide training speed-up in 3D deconvolution tasks.
  arXiv  Detail & Related papers  (2023-08-18T09:51:11Z)
• GRIG: Few-Shot Generative Residual Image Inpainting [27.252855062283825]
  We present a novel few-shot generative residual image inpainting method that produces high-quality inpainting results. The core idea is to propose an iterative residual reasoning method that incorporates Convolutional Neural Networks (CNNs) for feature extraction. We also propose a novel forgery-patch adversarial training strategy to create faithful textures and detailed appearances.
  arXiv  Detail & Related papers  (2023-04-24T12:19:06Z)
• DELAD: Deep Landweber-guided deconvolution with Hessian and sparse prior [0.22940141855172028]
  We present a model for non-blind image deconvolution that incorporates the classic iterative method into a deep learning application. We build our network based on the iterative Landweber deconvolution algorithm, which is integrated with trainable convolutional layers to enhance the recovered image structures and details.
  arXiv  Detail & Related papers  (2022-09-30T11:15:03Z)
• SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurring [23.86692375792203]
  Image deblurring is a computer vision problem that aims to recover a sharp image from a blurred image. Our model uses dilated convolution to enable the obtainment of the large receptive field with high spatial resolution. We propose a novel module using the wavelet transform, which effectively helps the network to recover clear high-frequency texture details.
  arXiv  Detail & Related papers  (2021-10-12T07:58:10Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

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.