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..
Autoregressive Image Generation without Vector Quantization
Authors: Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, Kaiming He
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment on Image Net [9] at a resolution of 256 256. We evaluate FID [22] and IS [43], and provide Precision and Recall as references following common practice [10]. |
| Researcher Affiliation | Collaboration | 1MIT CSAIL 2Google DeepMind 3Tsinghua University |
| Pseudocode | Yes | Pseudo-code of Diffusion Loss. See Algorithm 1. Algorithm 1 Diffusion Loss: PyTorch-like Pseudo-code |
| Open Source Code | Yes | Code is available at https://github.com/LTH14/mar. |
| Open Datasets | Yes | We experiment on Image Net [9] at a resolution of 256 256. |
| Dataset Splits | No | The paper uses ImageNet but does not explicitly provide specific training/validation/test dataset splits (e.g., percentages or sample counts) within the text of the paper. |
| Hardware Specification | Yes | Our training is mainly done on 16 servers with 8 V100 GPUs each. |
| Software Dependencies | No | The paper mentions using the Adam W optimizer and provides PyTorch-like pseudocode, but it does not specify version numbers for Python, PyTorch, or other relevant software libraries. |
| Experiment Setup | Yes | Our noise schedule has a cosine shape, with 1000 steps at training time; at inference time, it is resampled with fewer steps (by default, 100). By default, we use 3 blocks and a width of 1024 channels. By default, our Transformer has 32 blocks and a width of 1024... At training time, we randomly sample a masking ratio... in [0.7, 1.0]... By default, the models are trained using the Adam W optimizer for 400 epochs. The weight decay and momenta for Adam W are 0.02 and (0.9, 0.95). We use a batch size of 2048 and a learning rate (lr) of 8e-4. |