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..
Information-Theoretic Diffusion
Authors: Xianghao Kong, Rob Brekelmans, Greg Ver Steeg
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate ITD’s efficacy by evaluating its discovered concepts on downstream tasks and through human studies. On ImageNet, ITD achieves an average accuracy of 75.3% when clustering features from a self-supervised model, significantly outperforming alternative approaches. |
| Researcher Affiliation | Collaboration | Mengyu Yang1, Yi-Fan Zhang2, Ethan Liu2, Daniel M. Blatman2, Gregory M. P. O’Hare3, Georgios Tzimiropoulos1, Jie Song2 1 University of Oulu, Finland 2 Huawei Noah’s Ark Lab, Ireland 3 University College Dublin, Ireland 4 Huawei Noah’s Ark Lab, China |
| Pseudocode | Yes | Algorithm 1 Information-Theoretic Diffusion (ITD) |
| Open Source Code | No | Our code will be publicly released upon acceptance. |
| Open Datasets | Yes | ImageNet-1K (ILSVRC 2012) is a widely used benchmark dataset for image classification tasks, containing 1.28 million training images and 50,000 validation images across 1,000 classes. |
| Dataset Splits | Yes | ImageNet-1K (ILSVRC 2012) is a widely used benchmark dataset for image classification tasks, containing 1.28 million training images and 50,000 validation images across 1,000 classes. |
| Hardware Specification | Yes | All experiments were run on NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions software like PyTorch, scikit-learn, Faiss, and Diffusers, but does not specify their version numbers. |
| Experiment Setup | Yes | The backbone feature extractor is ViT-B/16 from DINO [5], pre-trained on ImageNet-1K. We extract features from the penultimate layer of the backbone model. We use AdamW optimizer with a learning rate of 1e-4 and a batch size of 256. The diffusion process runs for 2000 steps with a linear noise schedule, and the number of clusters K is set to 1000 for ImageNet. |