# CNeRV: Content-adaptive Neural Representation for Visual Data

arXiv: https://arxiv.org/abs/2211.10421 · HTML: https://mlsanity.com/paper/2211.10421/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 48 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 29 of 29)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Adversarial Feature Learning](https://mlsanity.com/paper/1605.09782/) (2016)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020)
- [Large Scale Adversarial Representation Learning](https://mlsanity.com/paper/1907.02544/) (2019)
- [COIN: COmpression with Implicit Neural representations](https://mlsanity.com/paper/2103.03123/) (2021)
- [Adversarially Learned Inference](https://mlsanity.com/paper/1606.00704/) (2016)
- [Video Compression through Image Interpolation](https://mlsanity.com/paper/1804.06919/) (2018)
- [Modulated Periodic Activations for Generalizable Local Functional Representations](https://mlsanity.com/paper/2104.03960/) (2021)
- [Variational Autoencoder for Deep Learning of Images, Labels and Captions](https://mlsanity.com/paper/1609.08976/) (2016)
- [Video Compression With Rate-Distortion Autoencoders](https://mlsanity.com/paper/1908.05717/) (2019)
- [Learned Video Compression](https://mlsanity.com/paper/1811.06981/) (2018)
- [Learning Disentangled Joint Continuous and Discrete Representations](https://mlsanity.com/paper/1804.00104/) (2018)
- [E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context](https://mlsanity.com/paper/2207.08132/) (2022)
- [ELF-VC: Efficient Learned Flexible-Rate Video Coding](https://mlsanity.com/paper/2104.14335/) (2021)
- [Conditional Entropy Coding for Efficient Video Compression](https://mlsanity.com/paper/2008.09180/) (2020)
- [Neural Video Coding using Multiscale Motion Compensation and Spatiotemporal Context Model](https://mlsanity.com/paper/2007.04574/) (2020)
- [Efficient Video Compression via Content-Adaptive Super-Resolution](https://mlsanity.com/paper/2104.02322/) (2021)
- [Neural Video Compression using Spatio-Temporal Priors](https://mlsanity.com/paper/1902.07383/) (2019)

## Built on by (most cited first, 21 of 21)

- [HNeRV: A Hybrid Neural Representation for Videos](https://mlsanity.com/paper/2304.02633/) (2023)
- [Towards Scalable Neural Representation for Diverse Videos](https://mlsanity.com/paper/2303.14124/) (2023)
- [Boosting Neural Representations for Videos with a Conditional Decoder](https://mlsanity.com/paper/2402.18152/) (2024)
- [DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codes](https://mlsanity.com/paper/2403.15679/) (2024)
- [Fast Encoding and Decoding for Implicit Video Representation](https://mlsanity.com/paper/2409.19429/) (2024)
- [Latent-INR: A Flexible Framework for Implicit Representations of Videos with Discriminative Semantics](https://mlsanity.com/paper/2408.02672/) (2024)
- [Implicit-explicit Integrated Representations for Multi-view Video Compression](https://mlsanity.com/paper/2311.17350/) (2023)
- [Continual Learning: Forget-free Winning Subnetworks for Video Representations](https://mlsanity.com/paper/2312.11973/) (2023)
- [FCNR: Fast Compressive Neural Representation of Visualization Images](https://mlsanity.com/paper/2407.16369/) (2024)
- [Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions](https://mlsanity.com/paper/2401.10217/) (2024)
- [Progressively Deformable 2D Gaussian Splatting for Video Representation at Arbitrary Resolutions](https://mlsanity.com/paper/2503.05600/) (2025)
- [Progressive Fourier Neural Representation for Sequential Video Compilation](https://mlsanity.com/paper/2306.11305/) (2023)
- [ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis](https://mlsanity.com/paper/2406.00227/) (2024)
- [MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidance](https://mlsanity.com/paper/2501.02427/) (2025)
- [How to Design and Train Your Implicit Neural Representation for Video Compression](https://mlsanity.com/paper/2506.24127/) (2025)
- [Predicting the Encoding Error of SIRENs](https://mlsanity.com/paper/2410.21645/) (2024)
- [Implicit Neural Representation Facilitates Unified Universal Vision Encoding](https://mlsanity.com/paper/2601.14256/) (2026)
- [MNeRV: A Multilayer Neural Representation for Videos](https://mlsanity.com/paper/2407.07347/) (2024)
- [NeR-VCP: A Video Content Protection Method Based on Implicit Neural Representation](https://mlsanity.com/paper/2408.15281/) (2024)
- [Releasing the Parameter Latency of Neural Representation for High-Efficiency Video Compression](https://mlsanity.com/paper/2410.01654/) (2024)
- [CoANeRV: Coordinate-Aware Token-Space Neural Video Representation](https://mlsanity.com/paper/2608.13938/) (2026)

## Abstract (arXiv)

Compression and reconstruction of visual data have been widely studied in the computer vision community, even before the popularization of deep learning. More recently, some have used deep learning to improve or refine existing pipelines, while others have proposed end-to-end approaches, including autoencoders and implicit neural representations, such as SIREN and NeRV. In this work, we propose Neural Visual Representation with Content-adaptive Embedding (CNeRV), which combines the generalizability of autoencoders with the simplicity and compactness of implicit representation. We introduce a novel content-adaptive embedding that is unified, concise, and internally (within-video) generalizable, that compliments a powerful decoder with a single-layer encoder. We match the performance of NeRV, a state-of-the-art implicit neural representation, on the reconstruction task for frames seen during training while far surpassing for frames that are skipped during training (unseen images). To achieve similar reconstruction quality on unseen images, NeRV needs 120x more time to overfit per-frame due to its lack of internal generalization. With the same latent code length and similar model size, CNeRV outperforms autoencoders on reconstruction of both seen and unseen images. We also show promising results for visual data compression. More details can be found in the project pagehttps://haochen-rye.github.io/CNeRV/