Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges

• URL: http://arxiv.org/abs/2004.01536v7
• Date: Tue, 18 May 2021 09:27:23 GMT
• Title: Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges
• Authors: Ylva Jansson and Tony Lindeberg
• Abstract summary: A straightforward approach for handling scale in a deep network is to process an image at several scales simultaneously in a set of scale channels. We propose a new type of foveated scale channel architecture, where the scale channels process increasingly larger parts of the image with decreasing resolution. Our proposed FovMax and FovAvg networks perform almost identically over a scale range of 8, also when training on single scale training data, and do also give improvements in the small sample regime.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to handle large scale variations is crucial for many real world visual tasks. A straightforward approach for handling scale in a deep network is to process an image at several scales simultaneously in a set of scale channels. Scale invariance can then, in principle, be achieved by using weight sharing between the scale channels together with max or average pooling over the outputs from the scale channels. The ability of such scale channel networks to generalise to scales not present in the training set over significant scale ranges has, however, not previously been explored. We, therefore, present a theoretical analysis of invariance and covariance properties of scale channel networks and perform an experimental evaluation of the ability of different types of scale channel networks to generalise to previously unseen scales. We identify limitations of previous approaches and propose a new type of foveated scale channel architecture, where the scale channels process increasingly larger parts of the image with decreasing resolution. Our proposed FovMax and FovAvg networks perform almost identically over a scale range of 8, also when training on single scale training data, and do also give improvements in the small sample regime.

Related papers

• Multi-scale Unified Network for Image Classification [33.560003528712414]
  CNNs face notable challenges in performance and computational efficiency when dealing with real-world, multi-scale image inputs. We propose Multi-scale Unified Network (MUSN) consisting of multi-scales, a unified network, and scale-invariant constraint. MUSN yields an accuracy increase up to 44.53% and diminishes FLOPs by 7.01-16.13% in multi-scale scenarios.
  arXiv  Detail & Related papers  (2024-03-27T06:40:26Z)
• Riesz networks: scale invariant neural networks in a single forward pass [0.7673339435080445]
  We introduce the Riesz network, a novel scale invariant neural network. As an application example, we consider detecting and segmenting cracks in tomographic images of concrete. We then validate its performance in segmenting simulated and real tomographic images featuring a wide range of crack widths.
  arXiv  Detail & Related papers  (2023-05-08T12:39:49Z)
• Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning [55.762840052788945]
  We present Scale-MAE, a pretraining method that explicitly learns relationships between data at different, known scales. We find that tasking the network with reconstructing both low/high frequency images leads to robust multiscale representations for remote sensing imagery.
  arXiv  Detail & Related papers  (2022-12-30T03:15:34Z)
• Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation [69.45176408639483]
  We reform the conv layer by resorting to the scale-space theory. We build a novel style named SCale AttentioN Conv Neural Network (textbfSCAN-CNN) As a single-shot scheme, the inference is more efficient than multi-shot fusion.
  arXiv  Detail & Related papers  (2022-09-19T06:35:04Z)
• Group Fisher Pruning for Practical Network Compression [58.25776612812883]
  We present a general channel pruning approach that can be applied to various complicated structures. We derive a unified metric based on Fisher information to evaluate the importance of a single channel and coupled channels. Our method can be used to prune any structures including those with coupled channels.
  arXiv  Detail & Related papers  (2021-08-02T08:21:44Z)
• Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales [0.0]
  We show that two previously proposed scale channel network designs do not generalise well to scales not present in the training set. We propose a new type of foveated scale channel architecture, where the scale channels process increasingly larger parts of the image with decreasing resolution. Our proposed FovMax and FovAvg networks perform almost identically over a scale range of 8, also when training on single scale training data, and do also give improved performance when learning from transformations with large scale variations in the small sample regime.
  arXiv  Detail & Related papers  (2021-06-11T14:22:26Z)
• Channel Scaling: A Scale-and-Select Approach for Transfer Learning [2.6304695993930594]
  Transfer learning with pre-trained neural networks is a common strategy for training classifiers in medical image analysis. We propose a novel approach to efficiently build small and well performing networks by introducing the channel-scaling layers. By imposing L1 regularization and thresholding on the scaling weights, this framework iteratively removes unnecessary feature channels from a pre-trained model.
  arXiv  Detail & Related papers  (2021-03-22T23:26:57Z)
• Scale-covariant and scale-invariant Gaussian derivative networks [0.0]
  This paper presents a hybrid approach between scale-space theory and deep learning, where a deep learning architecture is constructed by coupling parameterized scale-space operations in cascade. It is demonstrated that the resulting approach allows for scale generalization, enabling good performance for classifying patterns at scales not present in the training data.
  arXiv  Detail & Related papers  (2020-11-30T13:15:10Z)
• On Robustness and Transferability of Convolutional Neural Networks [147.71743081671508]
  Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. We study the interplay between out-of-distribution and transfer performance of modern image classification CNNs for the first time. We find that increasing both the training set and model sizes significantly improve the distributional shift robustness.
  arXiv  Detail & Related papers  (2020-07-16T18:39:04Z)
• Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
  This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-06T15:51:00Z)
• Crowd Counting via Hierarchical Scale Recalibration Network [61.09833400167511]
  We propose a novel Hierarchical Scale Recalibration Network (HSRNet) to tackle the task of crowd counting. HSRNet models rich contextual dependencies and recalibrating multiple scale-associated information. Our approach can ignore various noises selectively and focus on appropriate crowd scales automatically.
  arXiv  Detail & Related papers  (2020-03-07T10:06:47Z)

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.