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..
Boosting Generative Image Modeling via Joint Image-Feature Synthesis
Authors: Theodoros Kouzelis, Efstathios Karypidis, Ioannis Kakogeorgiou, Spyridon Gidaris, Nikos Komodakis
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluated in both conditional and unconditional settings, our method delivers substantial improvements in image quality and training convergence speed, establishing a new direction for representation-aware generative modeling. [...] 4 Experiments |
| Researcher Affiliation | Collaboration | Theodoros Kouzelis Archimedes, Athena RC National Technical University of Athens Efstathios Karypidis Archimedes, Athena RC National Technical University of Athens Ioannis Kakogeorgiou Archimedes, Athena RC IIT, NCSR "Demokritos" Spyros Gidaris valeo.ai Nikos Komodakis Archimedes, Athena RC University of Crete IACM-Forth |
| Pseudocode | No | The paper does not contain any section explicitly labeled "Pseudocode" or "Algorithm", nor are there any structured algorithm blocks presented as figures or within the main text. |
| Open Source Code | Yes | Project page and code: https://representationdiffusion.github.io/ |
| Open Datasets | Yes | We follow the standard training setup of Di T (Peebles & Xie, 2023) and Si T (Ma et al., 2024), training on Image Net at 256 256 resolution with a batch size of 256. |
| Dataset Splits | Yes | We follow the standard training setup of Di T (Peebles & Xie, 2023) and Si T (Ma et al., 2024), training on Image Net at 256 256 resolution with a batch size of 256. [...] To benchmark generative performance, we report Frechet Inception Distance (FID) (Heusel et al., 2017), s FID (Nash et al., 2021), Inception Score (IS) (Salimans et al., 2016), Precision (Pre.) and Recall (Rec.) (Kynkäänniemi et al., 2019) using 50k samples and the ADM s Tensor Flow evaluation suite (Dhariwal & Nichol, 2021). |
| Hardware Specification | Yes | For both training and sampling we use 8 NVIDIA A100 40GB GPUs. |
| Software Dependencies | No | The paper mentions using "ADM s Tensor Flow evaluation suite" but does not specify version numbers for TensorFlow or any other software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Table 8: Optimization details. The optimization hyperparameters for both Di T and Si T models. Batch Size 256 Optimizer Adam W LR 10 4 (β1, β2) (0.9, 0.999). [...] For Representation Guidance, we set pdrop = 0.2, the guidance scale to wr = 1.5 for B models and wr = 1.1 for XL models. |