Soft Superpixel Neighborhood Attention
Authors: Kent W Gauen, Stanley H. Chan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | SNA outperforms alternative local attention modules on image denoising, and we compare the superpixels learned from denoising with those learned with superpixel supervision. This section demonstrates the impressive benefit of superpixel neighborhood attention compared to standard neighborhood attention. To verify whether the improvement is due to the proposed method, we compare several variations of both attention methods. Section 5.2 compares different attention modules within a simple network architecture on Gaussian denoising, which empirically verifies the theoretical findings in Sections 4.3 and A.2. Section 5.3 compares the superpixel probabilities learned from the denoising loss function with superpixels learned through supervised training. |
| Researcher Affiliation | Academia | Kent Gauen Purdue University gauenk@purdue.edu Stanley Chan Purdue University stanchan@purdue.edu |
| Pseudocode | No | The paper contains equations like (3), (4), (5) for NA, H-SNA, and SNA, and describes the steps, but not in a pseudocode format. |
| Open Source Code | Yes | 1Code for this project is available at https://github.com/gauenk/spix_paper |
| Open Datasets | Yes | We train each network for 800 epochs using a batch size of 2 on the BSD500 dataset [36] using a learning rate of 2 10 4 with a decay factor of 1/2 at epochs 300 and 600. Testing datasets are Set5 [44], BSD100 [36], Urban100 [45], and Manga109 [46]. |
| Dataset Splits | No | We train each network for 800 epochs using a batch size of 2 on the BSD500 dataset [36]. Testing datasets are Set5 [44], BSD100 [36], Urban100 [45], and Manga109 [46]. |
| Hardware Specification | Yes | The code is implemented in Python using Pytorch, Numpy, Pandas, and CUDA and run using two NVIDIA Titan RTX GPUs and one RTX 3090 Ti GPU [40 43]. |
| Software Dependencies | Yes | The code is implemented in Python using Pytorch, Numpy, Pandas, and CUDA and run using two NVIDIA Titan RTX GPUs and one RTX 3090 Ti GPU [40 43]. Cuda, release: 10.2.89, 2020. |
| Experiment Setup | Yes | We train each network for 800 epochs using a batch size of 2 on the BSD500 dataset [36] using a learning rate of 2 10 4 with a decay factor of 1/2 at epochs 300 and 600. The network is optimized with Adam [39]. |