Rotated MNIST
18 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Rotated MNIST
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MNIST
Latest papers with no code
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring In Data
Equivariance is a nice property to have as it produces much more parameter efficient neural architectures and preserves the structure of the input through the feature mapping.
ICNN: INPUT-CONDITIONED FEATURE REPRESENTATION LEARNING FOR TRANSFORMATION-INVARIANT NEURAL NETWORK
And our proposed decoder network serves the purpose of reducing the transformation present in the input image by learning to construct a representative image of the input image class.
Visual Context-aware Convolution Filters for Transformation-invariant Neural Network
We propose a novel visual context-aware filter generation module which incorporates contextual information present in images into Convolutional Neural Networks (CNNs).
Fast Inference in Capsule Networks Using Accumulated Routing Coefficients
Afterward, the routing coefficients associated with the training examples are accumulated offline and used to create a set of "master" routing coefficients.
DIVA: Domain Invariant Variational Autoencoder
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.
Deformable Classifiers
In this paper, we design a framework for training deformable classifiers, where latent transformation variables are introduced, and a transformation of the object image to a reference instantiation is computed in terms of the classifier output, separately for each class.
Learning Steerable Filters for Rotation Equivariant CNNs
In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input.
Local Group Invariant Representations via Orbit Embeddings
We consider transformations that form a \emph{group} and propose an approach based on kernel methods to derive local group invariant representations.
