mlsanityData to 14 Oct 2026

Learning to Linearize Under Uncertainty

arXiv 1506.03011 ↗First posted 9 Jun 2015cs.CVcited by 25

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Abstract (arXiv)

Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision. However, a principled way in which to train such hierarchies in the unsupervised setting has remained elusive. In this work we suggest a new architecture and loss for training deep feature hierarchies that linearize the transformations observed in unlabeled natural video sequences. This is done by training a generative model to predict video frames. We also address the problem of inherent uncertainty in prediction by introducing latent variables that are non-deterministic functions of the input into the network architecture.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Jun 2015.

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Citing papers per year: 2021: 4; 2022: 5; 2023: 4; 2024: 6; 2025: 4; 2026: 2.

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  1. 2024BootsTAP: Bootstrapped Training for Tracking-Any-Pointcited by 120
  2. 2021Disentangling Hate in Online Memescited by 46
  3. 2026Temporal Straightening for Latent Planningcited by 38
  4. 2024Scaling 4D Representationscited by 30
  5. 2022Symmetry-Based Representations for Artificial and Biological General Intelligencecited by 26
  6. 2022Back to the Manifold: Recovering from Out-of-Distribution Statescited by 20
  7. 2022Unsupervised Representation Learning in Deep Reinforcement Learning: A Reviewcited by 18
  8. 2024Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inferencecited by 17
  9. 2021Localization with Sampling-Argmaxcited by 11
  10. 2022Unsupervised Multi-object Segmentation Using Attention and Soft-argmaxcited by 11
  11. 2025Chirality in Action: Time-Aware Video Representation Learning by Latent Straighteningcited by 11
  12. 2021PDWN: Pyramid Deformable Warping Network for Video Interpolationcited by 10
13 more
  1. 2024Learning predictable and robust neural representations by straightening image sequencescited by 9
  2. 2021Two-stage Rule-induction Visual Reasoning on RPMs with an Application to Video Predictioncited by 7
  3. 2024Pose-Aware Self-Supervised Learning with Viewpoint Trajectory Regularizationcited by 7
  4. 2022Minimalistic Unsupervised Learning with the Sparse Manifold Transformcited by 6
  5. 2023A polar prediction model for learning to represent visual transformationscited by 6
  6. 2026GEOPHYS: The Geometry of Physical Plausibilitycited by 3
  7. 2023Long Range Object-Level Monocular Depth Estimation for UAVscited by 2
  8. 2023Prior based Sampling for Adaptive LiDARcited by 2
  9. 2023Brain-like representational straightening of natural movies in robust feedforward neural networkscited by 2
  10. 2025Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modelingcited by 2
  11. 2025SAM 2++: Tracking Anything at Any Granularitycited by 1
  12. 2024Video Prediction Models as General Visual Encoderscited by 0
  13. 2025Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparisoncited by 0