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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Channel Gating Neural Networks
Authors: Weizhe Hua, Yuan Zhou, Christopher M. De Sa, Zhiru Zhang, G. Edward Suh
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that applying channel gating in state-of-the-art networks achieves 2.7-8.0 reduction in floating-point operations (FLOPs) and 2.0-4.4 reduction in off-chip memory accesses with a minimal accuracy loss on CIFAR-10. |
| Researcher Affiliation | Academia | Weizhe Hua EMAIL Yuan Zhou EMAIL Christopher De Sa EMAIL Zhiru Zhang EMAIL G. Edward Suh EMAIL |
| Pseudocode | No | The paper does not contain any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described in this paper. |
| Open Datasets | Yes | We first evaluate CGNets only with the activation-wise gate on CIFAR-10 [17] and Image Net (ILSVRC 2012) [4] datasets to compare the accuracy and FLOP reduction trade-off with prior arts. |
| Dataset Splits | No | The paper mentions using CIFAR-10 and Image Net datasets but does not explicitly provide specific details about the training, validation, and test splits (e.g., percentages or exact counts) beyond implying standard usage. |
| Hardware Specification | Yes | Platform Intel i7-7700k NVIDIA GTX 1080Ti ASIC |
| Software Dependencies | No | The paper mentions using 'Mx Net [2]' as a framework but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | We choose a uniform target threshold (T) and number of groups (G) for all CGNets for the experiments in Section 5.1 and 5.2. ...We leverage KD to improve the accuracy of CGNets on Image Net where a Res Net-50 model is used as the teacher of our Res Net-18 based CGNets with κ = 1 and λkd = 0.5. |