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..
DEPrune: Depth-wise Separable Convolution Pruning for Maximizing GPU Parallelism
Authors: Cheonjun Park, Mincheol Park, Hyunchan Moon, Myung Kuk Yoon, Seokjin Go, Suhyun Kim, Won Woo Ro
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results show that DEPrune achieves up to 3.74 practical speedup in DSConv inference on GPUs while maintaining the accuracy of Efficient Net-B0 on Image Net. We assess the effectiveness of DEPrune using Image Net [8] and CIFAR-10 [25]. For the validation of image classification, we assess our method with CNN models using DSConv: Mobile Net-V2 [43], Efficient Net-B0 [45], and Mobile Net-V3 [22]. |
| Researcher Affiliation | Collaboration | 1 Samsung Electronics 2 Yonsei University 3 Korea Institute of Science and Technology 4 LG Electronics 5 Ewha Womans University 6 Georgia Institute of Technology |
| Pseudocode | No | The paper describes methods through text and diagrams (e.g., Figure 4, 5, 7) but does not include explicit pseudocode or algorithm blocks labeled "Pseudocode" or "Algorithm". |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [No] . Justification: We are going to open source the code later. |
| Open Datasets | Yes | We assess the effectiveness of DEPrune using Image Net [8] and CIFAR-10 [25]. |
| Dataset Splits | No | The paper states it uses Image Net and CIFAR-10, but does not explicitly specify exact train/validation/test dataset split percentages or sample counts, nor does it refer to predefined splits with citations for these specific splits. |
| Hardware Specification | Yes | We evaluate DEPrune using NVIDIA RTX 2080 Ti GPUs [1]. |
| Software Dependencies | No | The paper mentions using "Pytorch framework [39]" and "NVIDIA CUTLASS [24]" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We perform fine-tuning with only 65 epochs after conducting pruning methods. We set a batch size of 256. We use SGD optimizer with the weight decay, 1 10 4, and the momentum as 0.9 for fine-tuning. The initial learning rate is set to 0.001 and divided by 10 every 30 epoch. |