Denoising Diffusion Path: Attribution Noise Reduction with An Auxiliary Diffusion Model
Authors: Yiming Lei, Zilong Li, Junping Zhang, Hongming Shan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that DDPath can significantly reduce noise in the attributions resulting in clearer explanations and achieves better quantitative results than traditional path-based methods. 5 Experiments 5.1 Experimental Setup Datasets. Following previous studies [10, 11, 12], we evaluate the effectiveness of DDPath on the validation set of Image Net-1k [30] that contains 50, 000 images of 1, 000 classes. Furthermore, we conducted a pointing game experiment on MS COCO validation set [31]. |
| Researcher Affiliation | Academia | Yiming Lei1, Zilong Li1, Junping Zhang1, Hongming Shan2 1 Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University 2 Institute of Science and Technology for Brain-Inspired Intelligence & MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence & MOE Frontiers Center for Brain Science, Fudan University |
| Pseudocode | Yes | Algorithm 1 Algorithm of DDPath-IG. Require: Target image x and its label y, the initial noisy baseline x randomly sampled from a Gaussian; target model F( ), diffusion trained classifier hϕ, the diffusion model Eθ, total number of step T. Return: Attribution for the target image x: A = 1 T PT 1 t=0 gt. |
| Open Source Code | No | The data employed in this study are the publicly available, and the underlying source code will be released later. |
| Open Datasets | Yes | Datasets. Following previous studies [10, 11, 12], we evaluate the effectiveness of DDPath on the validation set of Image Net-1k [30] that contains 50, 000 images of 1, 000 classes. Furthermore, we conducted a pointing game experiment on MS COCO validation set [31]. |
| Dataset Splits | Yes | Datasets. Following previous studies [10, 11, 12], we evaluate the effectiveness of DDPath on the validation set of Image Net-1k [30] that contains 50, 000 images of 1, 000 classes. Furthermore, we conducted a pointing game experiment on MS COCO validation set [31]. |
| Hardware Specification | Yes | All the experiments are implemented by Py Torch [34] and conducted on an NVIDIA A100 GPU. |
| Software Dependencies | No | All the experiments are implemented by Py Torch [34] and conducted on an NVIDIA A100 GPU. (Only PyTorch is mentioned, without a specific version number). |
| Experiment Setup | Yes | The number of sampling steps for DDPath methods is 250. The above experiments involving DDPath used ρ = 1 t T , κ = t T by default, which maintains a stably decreased weight for sampling mean. Here, we reverse such scaling as ρ = t T , κ = 1 t T to enable a smaller mean and larger variance at the initial steps. |