Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
Authors: Yi Yu, Yufei Wang, Song Xia, Wenhan Yang, Shijian Lu, Yap-Peng Tan, Alex Kot
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the remarkable performance of our method across CIFAR-10, CIFAR-100, and a 100-class Image Net-subset. |
| Researcher Affiliation | Collaboration | 1 Rapid-Rich Object Search Lab, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore 2 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 3 Peng Cheng Laboratory, Shenzhen, China 4 School of Computer Science and Engineering, Nanyang Technological University, Singapore. |
| Pseudocode | Yes | Algorithm 1 Two-stage purification framework of unlearnable examples with D-VAE |
| Open Source Code | Yes | Code is available at https: //github.com/yuyi-sd/D-VAE. |
| Open Datasets | Yes | We choose three commonly used datasets: CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009), and a subset of Image Net (Deng et al., 2009) with the first 100 classes. |
| Dataset Splits | No | The paper mentions 'clean training dataset T' and 'clean test dataset D', but does not explicitly detail train/validation/test splits with percentages or sample counts for reproducibility. It also does not mention a separate validation set being used for hyperparameter tuning. |
| Hardware Specification | Yes | It s important to note that the times are recorded using CIFAR-10 as the dataset, Py Torch as the platform, and a single Nvidia RTX 3090 as the GPU. |
| Software Dependencies | No | Py Torch as the platform. (Only 'Py Torch' is mentioned without a version number or other specific software dependencies with versions). |
| Experiment Setup | Yes | For CIFAR-10, we use 60 epochs, while for CIFAR-100 and the Image Net, 100 epochs are allowed. In all experiments, we use SGD optimizer with an initial learning rate of 0.1 and the Cosine Annealing LR scheduler, keeping a consistent batch size of 128. For D-VAE training on unlearnable CIFAR-10, we use a KLD target of 1.0 in the first stage and 3.0 in the second stage, with only a single 0.5 downsampling to preserve image quality. For the CIFAR-100, we maintain the same hyperparameters as CIFAR-10, except for setting kld2 to 4.5. For Image Net-subset, which has higher-resolution images, we employ more substantial downsampling ( 0.125) in the first stage and set a KLD target of 1.5, while the second stage remains the same as with CIFAR. |