# Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios

arXiv: https://arxiv.org/abs/2406.06165 · HTML: https://mlsanity.com/paper/2406.06165/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 37 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [Ladder Variational Autoencoders](https://mlsanity.com/paper/1602.02282/) (2016)
- [Channel-wise Autoregressive Entropy Models for Learned Image Compression](https://mlsanity.com/paper/2007.08739/) (2020)
- [Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images](https://mlsanity.com/paper/2011.10650/) (2020)
- [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023)
- [Checkerboard Context Model for Efficient Learned Image Compression](https://mlsanity.com/paper/2103.15306/) (2021)
- [Variable Rate Image Compression with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06085/) (2015)
- [Context-adaptive Entropy Model for End-to-end Optimized Image Compression](https://mlsanity.com/paper/1809.10452/) (2018)
- [BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling](https://mlsanity.com/paper/1902.02102/) (2019)
- [Image and Video Compression with Neural Networks: A Review](https://mlsanity.com/paper/1904.03567/) (2019)
- [MLIC: Multi-Reference Entropy Model for Learned Image Compression](https://mlsanity.com/paper/2211.07273/) (2022)
- [An Introduction to Neural Data Compression](https://mlsanity.com/paper/2202.06533/) (2022)
- [Artificial Intelligence in the Creative Industries: A Review](https://mlsanity.com/paper/2007.12391/) (2020)
- [Lossy Image Compression with Quantized Hierarchical VAEs](https://mlsanity.com/paper/2208.13056/) (2022)
- [QARV: Quantization-Aware ResNet VAE for Lossy Image Compression](https://mlsanity.com/paper/2302.08899/) (2023)
- [Expanded Adaptive Scaling Normalization for End to End Image Compression](https://mlsanity.com/paper/2208.03049/) (2022)
- [Robust Wind Turbine Blade Segmentation from RGB Images in the Wild](https://mlsanity.com/paper/2306.14810/) (2023)
- [Comprehensive Complexity Assessment of Emerging Learned Image Compression on CPU and GPU](https://mlsanity.com/paper/2212.05466/) (2022)
- [A novel Cross-Component Context Model for End-to-End Wavelet Image Coding](https://mlsanity.com/paper/2303.05121/) (2023)

## Built on by (most cited first, 7 of 7)

- [Dual-Space Augmented Intrinsic-LoRA for Wind Turbine Segmentation](https://mlsanity.com/paper/2412.20838/) (2024)
- [Unsupervised Modular Adaptive Region Growing and RegionMix Classification for Wind Turbine Segmentation](https://mlsanity.com/paper/2601.04065/) (2026)
- [Discriminant Learning-based Colorspace for Blade Segmentation](https://mlsanity.com/paper/2601.13816/) (2026)
- [Probabilistic Deep Discriminant Analysis for Wind Blade Segmentation](https://mlsanity.com/paper/2601.13852/) (2026)
- [Meteorological data and Sky Images meets Neural Models for Photovoltaic Power Forecasting](https://mlsanity.com/paper/2602.15782/) (2026)
- [End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines](https://mlsanity.com/paper/2603.29927/) (2026)
- [Deep Image Segmentation via Discriminant Feature Learning](https://mlsanity.com/paper/2605.14609/) (2026)

## Abstract (arXiv)

Rate-distortion optimization through neural networks has accomplished competitive results in compression efficiency and image quality. This learning-based approach seeks to minimize the compromise between compression rate and reconstructed image quality by automatically extracting and retaining crucial information, while discarding less critical details. A successful technique consists in introducing a deep hyperprior that operates within a 2-level nested latent variable model, enhancing compression by capturing complex data dependencies. This paper extends this concept by designing a generalized L-level nested generative model with a Markov chain structure. We demonstrate as L increases that a trainable prior is detrimental and explore a common dimensionality along the distinct latent variables to boost compression performance. As this structured framework can represent autoregressive coders, we outperform the hyperprior model and achieve state-of-the-art performance while reducing substantially the computational cost. Our experimental evaluation is performed on wind turbine scenarios to study its application on visual inspections