Training-Free Bottleneck Width Planning for Convolutional Autoencoders

arXiv:2609.30755 · cs.CV · Submitted 2026-09-25 · Read on arXiv

cs.CV

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/GGN-2015/mssrd

Terminology

• Benchmark — 6× in this paper · explained in 2 episodes
• Intrinsic Dimension — 4× in this paper · explained in 2 episodes
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Latent Space — 2× in this paper · explained in 7 episodes
• Principal Component Analysis (PCA) — 2× in this paper · explained in 4 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Pruning — 1× in this paper · explained in 4 episodes

Sources

• FONDUE: an algorithm to find the optimal dimensionality of the latent representations of variational autoencoders
• Deep Learning for Classical Japanese Literature
• Gaussian Error Linear Units (GELUs)
• Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction
• The Intrinsic Dimension of Images and Its Impact on Learning
• Rate-Distortion Auto-Encoders
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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