Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression
Authors: Xi Zhang, Xiaolin Wu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experimentation on standard benchmark datasets, we demonstrate the effectiveness of our approach in significantly improving the compression performance of DNN-based image compression systems. Our method outperforms existing DNN quantization schemes in terms of both rate-distortion performance and computational complexity, increasing the cost-effectiveness of DNN image compression models. |
| Researcher Affiliation | Academia | Xi Zhang1 Xiaolin Wu2 1Department of Electronic Engineering, Shanghai Jiao Tong University 2School of Computing and Artificial Intelligence, Southwest Jiaotong University EMAIL, EMAIL |
| Pseudocode | Yes | A.4.1 Mathematical Formulation: Given a lattice Λ with a basis B = [b1, b2, . . . , bn], we want to find a lattice point v Rn that is close to a target vector t Rn. Babai s rounding algorithm involves the following steps: 1. Compute the Gram-Schmidt orthogonalization of the basis B, resulting in an orthogonal basis B = [b 1, b 2, . . . , b n]. 2. Express the target vector t in terms of the orthogonal basis B : where ci are the coordinates of t in the Gram-Schmidt basis B . 3. Round each coordinate ci to the nearest integer: c = (round(c1), round(c2), . . . , round(cn)) 4. Construct the approximate lattice point: i=1 round(ci)bi |
| Open Source Code | Yes | The source code will be available before the Neur IPS conference. |
| Open Datasets | Yes | The training dataset comprises high-quality images carefully selected from the Image Net dataset [15]. The trained compression models are evaluated on two widely used datasets: the Kodak dataset [14] and the CLIC validation set [8]. |
| Dataset Splits | No | The paper mentions training on the ImageNet dataset and evaluation on the Kodak dataset and CLIC validation set, but it does not specify explicit training/validation/test splits (e.g., percentages or counts) for the datasets used in the experiments. |
| Hardware Specification | Yes | All experiments are conducted with four RTX 3090 GPUs. |
| Software Dependencies | No | All modules including the proposed learnable lattice vector quantization modules are implemented in Py Torch and Compress AI [9]. The paper mentions the software used (PyTorch and Compress AI) but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | Each network and context model combination is trained for 3 million iterations. We train each model using the Adam optimizer with β 1 = 0.9, β2 = 0.999. The initial learning rate is set to 10⁻⁴ for the first 2M iterations, and then decayed to 10⁻⁵ for another 1M iterations training. Training images are then random-cropped to 256 × 256 and batched into 16. |