Rotated MNIST

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

20 shown of 20 papers with code (41 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• DIVA: Domain Invariant Variational Autoencoders
  24 May 2019 4 repositories listed Syntology ran 0 of 4 samples · 4 unverified · 4 pointer-only (licence)
  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
  20 Jul 2020 3 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  In implementation, we discretize the system using the numerical schemes of PDOs, deriving approximately equivariant convolutions (PDO-eConvs).
• Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
  18 Jun 2021 2 repositories listed
  Training a Convolutional Neural Network (CNN) to be robust against rotation has mostly been done with data augmentation.
• Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
  28 Mar 2020 2 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  The domain specific components are discarded after training and only the common component is retained.
• Deep Rotation Equivariant Network
  24 May 2017 2 repositories listed
  Recently, learning equivariant representations has attracted considerable research attention.
• On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory
  16 Dec 2024 1 repository listed Syntology ran 0 of 13 samples · 13 unverified · 13 pointer-only (licence)
  In this work, we aim to understand when and how deep networks can learn symmetries from data.
• ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding
  2 Jun 2024 1 repository listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  Normalizing flow-based generative models have been widely used in applications where the exact density estimation is of major importance.
• Artificial Neuronal Ensembles with Learned Context Dependent Gating
  17 Jan 2023 1 repository listed
  Finally, there is a regularization term responsible for ensuring that new tasks are encoded in gates that are as orthogonal as possible from previously used ones.
• Learning unfolded networks with a cyclic group structure
  16 Nov 2022 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  Deep neural networks lack straightforward ways to incorporate domain knowledge and are notoriously considered black boxes.
• Learning Invariant Representations for Equivariant Neural Networks Using Orthogonal Moments
  22 Sep 2022 1 repository listed
  The final classification layer in equivariant neural networks is invariant to different affine geometric transformations such as rotation, reflection and translation, and the scalar value is obtained by either…
• Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
  25 Oct 2021 1 repository listed
  In addition, thanks to the increase in computational efficiency, we are able to implement G-CNNs equivariant to the Sim(2) group; the group of dilations, rotations and translations.
• Learning Partial Equivariances from Data
  19 Oct 2021 1 repository listed Syntology ran 0 of 4 samples · 4 unverified · 4 pointer-only (licence)
  Frequently, transformations occurring in data can be better represented by a subset of a group than by a group as a whole, e.
• CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers
  21 Jul 2020 1 repository listed
  Deep Convolutional Neural Networks (CNNs) are empirically known to be invariant to moderate translation but not to rotation in image classification.
• Domain Generalization using Causal Matching
  12 Jun 2020 1 repository listed Syntology ran 4 of 9 samples · 5 unverified
  In the domain generalization literature, a common objective is to learn representations independent of the domain after conditioning on the class label.
• VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing
  1 Jan 2020 1 repository listed
  Deep Neural Networks (DNNs) achieve the state-of-the-art results on a wide range of image processing tasks, however, the majority of such solutions are problem-specific, like most AI algorithms.
• General E(2)-Equivariant Steerable CNNs
  1 Dec 2019 1 repository listed
  Here we give a general description of E(2)-equivariant convolutions in the framework of Steerable CNNs.
• CapsGAN: Using Dynamic Routing for Generative Adversarial Networks
  7 Jun 2018 1 repository listed
  We show that CapsGAN performs better than or equal to traditional CNN based GANs in generating images with high geometric transformations using rotated MNIST.
• Polar Transformer Networks
  6 Sep 2017 1 repository listed
  The result is a network invariant to translation and equivariant to both rotation and scale.
• Harmonic Networks: Deep Translation and Rotation Equivariance
  14 Dec 2016 1 repository listed Syntology ran 0 of 13 samples · 13 unverified
  This is not the case for rotations.
• Group Equivariant Convolutional Networks
  24 Feb 2016 1 repository listed
  We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.