Learning Instance-wise Sparsity for Accelerating Deep Models
Authors: Chuanjian Liu, Yunhe Wang, Kai Han, Chunjing Xu, Chang Xu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on benchmark datasets and networks demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | 1Huawei Noah s Ark Lab 2School of Computer Science, FEIT, University of Sydney, Australia |
| Pseudocode | No | The paper does not contain STRUCTURED PSEUDOCODE OR ALGORITHM BLOCKS (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide CONCRETE ACCESS TO SOURCE CODE (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We extensively evaluate our methods on two popular classification datasets: CIFAR-10 [Krizhevsky, 2009] and Imagenet(ILSVRC2012) [Deng et al., 2009]. |
| Dataset Splits | No | The paper uses well-known datasets (CIFAR-10, ImageNet) and mentions 'CIFAR-10 test set', but does not explicitly provide specific percentages, sample counts, or citations for training, validation, and test splits needed for full reproducibility of the data partitioning, specifically for a validation set. |
| Hardware Specification | No | The paper does not provide SPECIFIC HARDWARE DETAILS (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide SPECIFIC ANCILLARY SOFTWARE DETAILS (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | A number of ℓ2,1-norm regularization factors are considered, λ = 0, 1e-6, 1e-7, 1e-8 respectively. We set a global CV threshold as α... and set a drop threshold β [0, 2). |