Masked Swingers: Harnessing Data Augmentation to Advance Autoencoders for Self-Supervised Learning

arXiv:2609.38278 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/ax-ml/jax

Terminology

• Accuracy — 22× in this paper · explained in 1 episode
• Data Augmentation — 10× in this paper · explained in 7 episodes
• Self-Supervised Learning — 10× in this paper · explained in 7 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Decode — 6× in this paper · explained in 2 episodes
• Representation Learning — 6× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Adam — 5× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Embeddings — 5× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Self-distillation — 2× in this paper · explained in 6 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning (SSL) — 1× in this paper · explained in 4 episodes

Sources

• LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
• MultiGrain: a unified image embedding for classes and instances
• TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings
• MotionJEPA: Preventing Temporal Feature Collapse by Capturing Visual Changes in Latent Space
• Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality
• Ti-MAE: Self-Supervised Masked Time Series Autoencoders
• PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling
• The 2017 DAVIS Challenge on Video Object Segmentation
• Gaussian Masked Autoencoders
• The information bottleneck method
• A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
• iBOT: Image BERT Pre-Training with Online Tokenizer

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