Corpus ID: 6099034

Spatial Transformer Networks

@inproceedings{Jaderberg2015SpatialTN,
  title={Spatial Transformer Networks},
  author={Max Jaderberg and Karen Simonyan and Andrew Zisserman and Koray Kavukcuoglu},
  booktitle={NIPS},
  year={2015}
}
Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. [...] Key Method This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the…Expand
Spatial Transformations in Deep Neural Networks
  • Michal Bednarek, K. Walas
  • Computer Science
  • 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)
  • 2018
TLDR
This paper introduces the end-to-end system that is able to learn spatial invariance including in-plane and out-of-plane rotations and shows that it can successfully improve the classification score by implementing so-called Spatial Transformer module. Expand
Deep Diffeomorphic Transformer Networks
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This work investigates the use of flexible diffeomorphic image transformations within neural networks and demonstrates that significant performance gains can be attained over currently-used models. Expand
A Refined Spatial Transformer Network
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Studying Invariances of Trained Convolutional Neural Networks
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A new learnable module, the Invariant Transformer Net, is introduced, which enables us to learn differentiable parameters for a set of affine transformations, which allows us to extract the space of transformations to which the CNN is invariant and its class prediction robust. Expand
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This work proposes a loss function defined between the warped features of pairs of instances, which improves the localization ability of VTN and consistently boosts the features' representation power and consequently the networks' accuracy on fine-grained image recognition and instance-level image retrieval. Expand
SPATIAL TRANSFORMATIONS
Convolutional Neural Networks (CNNs) are extremely efficient, since they exploit the inherent translation-invariance of natural images. However, translation is just one of a myriad of useful spatialExpand
DeSTNet: Densely Fused Spatial Transformer Networks
TLDR
This paper proposes Densely Fused Spatial Transformer Network (DeSTNet), which, to the best knowledge, is the first dense fusion pattern for combining multiple STNs, and shows how changing the connectivity pattern of multipleSTNs from sequential to dense leads to more powerful alignment modules. Expand
DeSTNet : Densely Fused Spatial Transformer Networks 1
Modern Convolutional Neural Networks (CNN) are extremely powerful on a range of computer vision tasks. However, their performance may degrade when the data is characterised by large intra-classExpand
Exploiting Cyclic Symmetry in Convolutional Neural Networks
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This work introduces four operations which can be inserted into neural network models as layers, andWhich can be combined to make these models partially equivariant to rotations, and which enable parameter sharing across different orientations. Expand
Warped Convolutions: Efficient Invariance to Spatial Transformations
TLDR
This work presents a construction that is simple and exact, yet has the same computational complexity that standard convolutions enjoy, consisting of a constant image warp followed by a simple convolution, which are standard blocks in deep learning toolboxes. Expand
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