Interpreting and Disentangling Feature Components of Various Complexity from DNNs
Authors: Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Datasets, DNNs & Implementation details. We used our method to analyze VGG-16 (Simonyan et al., 2017) and Res Net-8/14/18/20/32/34/44 (He et al., 2016).3 For simplicity, we limited our attention to coarse-grained and finegrained classification. We trained these DNNs based on the CIFAR-10 dataset (Krizhevsky et al., 2009) and the CUB200-2011 dataset (Wah et al., 2011). |
| Researcher Affiliation | Academia | 1Shanghai Jiao Tong University. 2Quanshi Zhang is the corresponding author. He is with the John Hopcroft Center and the Mo E Key Lab of Artificial Intelligence, AI Institute, at the Shanghai Jiao Tong University, China. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We trained these DNNs based on the CIFAR-10 dataset (Krizhevsky et al., 2009) and the CUB200-2011 dataset (Wah et al., 2011). |
| Dataset Splits | No | The paper mentions using different numbers of training samples and a 'test set', but it does not provide explicit training/validation/test dataset splits (e.g., percentages, counts, or references to predefined splits for the main DNN training). It mentions a cross-validation setup for a regressor, but not for the primary DNN training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud computing instances) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with version numbers like Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | We design decomposer nets Φ(1)(x), . . . , Φ(L)(x) with residual architectures. The decomposer net consists of three types of residual blocks, each type having m blocks. Each block of the three types consists of a Re LU layer and a convolutional layer with 128γ, 256γ, 512γ channels, respectively. In most experiments, we set γ = 1, but in Figure 3(a), we try different values of γ to test decomposer nets of different widths. ... To boost the learning efficiency, we used parameters of the learned Φ(li) to initialize first li layers in Φ(li+1). |