# LCIP: Loss-Controlled Inverse Projection of High-Dimensional Image Data

arXiv: https://arxiv.org/abs/2602.11141 · HTML: https://mlsanity.com/paper/2602.11141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 53 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 22 of 22)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [Intrinsic dimension of data representations in deep neural networks](https://mlsanity.com/paper/1905.12784/) (2019)
- [Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold](https://mlsanity.com/paper/2305.10973/) (2023)
- [Controllable Invariance through Adversarial Feature Learning](https://mlsanity.com/paper/1705.11122/) (2017)
- [Invariant Representations without Adversarial Training](https://mlsanity.com/paper/1805.09458/) (2018)
- [Disentangling factors of variation in deep representations using adversarial training](https://mlsanity.com/paper/1611.03383/) (2016)
- [A survey of dimensionality reduction techniques](https://mlsanity.com/paper/1403.2877/) (2014)
- [t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections](https://mlsanity.com/paper/2002.06910/) (2020)
- [Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions](https://mlsanity.com/paper/1812.09502/) (2018)
- [Unsupervised Adversarial Invariance](https://mlsanity.com/paper/1809.10083/) (2018)
- [Deep Learning Multidimensional Projections](https://mlsanity.com/paper/1902.07958/) (2019)
- [A Two-Step Disentanglement Method](https://mlsanity.com/paper/1709.00199/) (2017)
- [UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data](https://mlsanity.com/paper/2111.01744/) (2021)
- [Effect of Input Noise Dimension in GANs](https://mlsanity.com/paper/2004.06882/) (2020)
- [Unified Adversarial Invariance](https://mlsanity.com/paper/1905.03629/) (2019)
- [Evaluating Autoencoders for Parametric and Invertible Multidimensional Projections](https://mlsanity.com/paper/2504.16831/) (2025)
- [Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations](https://mlsanity.com/paper/2304.07686/) (2023)

## Abstract (arXiv)

Projections (or dimensionality reduction) methods P aim to map high-dimensional data to typically 2D scatterplots for visual exploration. Inverse projection methods P-1 aim to map this 2D space to the data space to support tasks such as data augmentation, classifier analysis, and data imputation. Current P-1 methods suffer from a fundamental limitation -- they can only generate a fixed surface-like structure in data space, which poorly covers the richness of this space. We address this by a new method that can `sweep' the data space under user control. Our method works generically for any P technique and dataset, is controlled by two intuitive user-set parameters, and is simple to implement. We demonstrate it by an extensive application involving image manipulation for style transfer.