Logit Perturbation
Authors: Mengyang Li, Fengguang Su, Ou Wu, Ji Zhang1359-1366
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark image classification data sets and their long-tail versions indicated the competitive performance of our learning method. In addition, existing methods can be further improved by utilizing our method. |
| Researcher Affiliation | Collaboration | Mengyang Li1,2, Fengguang Su2, Ou Wu2*, Ji Zhang3 1 Jiuantianxia Inc., China 2 National Center for Applied Mathematics, Tianjin University, China 3 The University of Southern Queensland, Australia |
| Pseudocode | Yes | Algorithm 1: Learning to Perturb Logits (LPL) and Algorithm 2: PGD-like Optimization are provided, detailing the steps of the proposed methods. |
| Open Source Code | Yes | All the codes are available online2. 2https://github.com/limengyang1992/lpl |
| Open Datasets | Yes | In this subsection, two benchmark image classification data sets, namely, CIFAR10 and CIFAR100, are used. Both data consist of 32 32 natural images in 10 classes for CIFAR10 and 100 classes for CIFAR100. There are 50,000 images for training and 10,000 images for testing. |
| Dataset Splits | Yes | There are 50,000 images for training and 10,000 images for testing. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running experiments, such as CPU or GPU models, or cloud computing resources. |
| Software Dependencies | No | The paper does not list specific software dependencies, libraries, or their version numbers used in the implementation or for conducting experiments. |
| Experiment Setup | Yes | The PGD-like optimization in Algorithm 1 contains two hyper-parameters, namely, step size and #steps. Let α be the step size, and Kc be the number of steps(#steps) for category c. On the balanced classification, the α is searched in {0.01, 0.02, 0.03}. the Kc is calculated by Eq. (19). |