Rotated MNIST
18 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Rotated MNIST
| Trend | Dataset | Best Model | Paper | Code | Compare |
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MNIST
Most implemented papers
DIVA: Domain Invariant Variational Autoencoders
We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain.
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
shenzy08/PDO-eConvs
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ICML 2020
In implementation, we discretize the system using the numerical schemes of PDOs, deriving approximately equivariant convolutions (PDO-eConvs).
Deep Rotation Equivariant Network
Recently, learning equivariant representations has attracted considerable research attention.
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
vihari/csd
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ICML 2020
The domain specific components are discarded after training and only the common component is retained.
Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
Training a Convolutional Neural Network (CNN) to be robust against rotation has mostly been done with data augmentation.
Group Equivariant Convolutional Networks
adambielski/pytorch-gconv-experiments
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We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.
Harmonic Networks: Deep Translation and Rotation Equivariance
deworrall92/harmonicConvolutions
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CVPR 2017
This is not the case for rotations.
Polar Transformer Networks
daniilidis-group/polar-transformer-networks
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ICLR 2018
The result is a network invariant to translation and equivariant to both rotation and scale.
CapsGAN: Using Dynamic Routing for Generative Adversarial Networks
We show that CapsGAN performs better than or equal to traditional CNN based GANs in generating images with high geometric transformations using rotated MNIST.
General E(2)-Equivariant Steerable CNNs
QUVA-Lab/e2cnn
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NeurIPS 2019
Here we give a general description of E(2)-equivariant convolutions in the framework of Steerable CNNs.
