Utilizing Class Information for Deep Network Representation Shaping
Authors: Daeyoung Choi, Wonjong Rhee3396-3403
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed an extensive set of experiments on the most popular datasets (MNIST, CIFAR-10, CIFAR-100) and architectures (MLP, CNN). Additionally, Res Net (He et al. 2016) was tested as an example of a sophisticated network, and an image reconstruction task using autoencoder was tested as an example of a different type of task. We have tested a variety of scenarios with different optimizers, number of classes, network size, and data size. The results show that our representation regularizers outperform the baseline (no regularizer) and L1/L2 weight regularizers for almost all the scenarios that we have tested. |
| Researcher Affiliation | Academia | Daeyoung Choi, Wonjong Rhee Department of Transdisciplinary Studies Seoul National University Seoul, 08826, South Korea {choid, wrhee}@snu.ac.kr |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is made available at https://github.com/snu-adsl/classwiseregularizer. |
| Open Datasets | Yes | Three popular datasets (MNIST, CIFAR-10, and CIFAR100) were used as benchmarks. |
| Dataset Splits | Yes | When a regularizer (including L1W and L2W) was used for an evaluation scenario, the penalty loss weight λ was determined as one of {0.001, 0.01, 0.1, 1, 10, 100} using 10,000 validation samples. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU, CPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | When a regularizer (including L1W and L2W) was used for an evaluation scenario, the penalty loss weight λ was determined as one of {0.001, 0.01, 0.1, 1, 10, 100} using 10,000 validation samples... Mini-batch size was increased to 500 for CIFAR-100 such that classwise operations can be appropriately performed but was kept at the default value of 100 for MNIST and CIFAR-10. |