Real-Fake: Effective Training Data Synthesis Through Distribution Matching
Authors: Jianhao Yuan, Jie Zhang, Shuyang Sun, Philip Torr, Bo Zhao
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we demonstrate the effectiveness of our synthetic data across diverse image classification tasks, both as a replacement for and augmentation to real datasets |
| Researcher Affiliation | Collaboration | University of Oxford ETH Zurich Beijing Academy of Artificial Intelligence |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Code released at: https://github.com/BAAI-DCAI/Training-Data-Synthesis. |
| Open Datasets | Yes | Datasets. We conduct benchmark experiments with Res Net50 (He et al., 2016) across three Image Net datasets: Image Nette (IN-10) (Howard, 2019), Image Net100 (IN-100) (Tian et al., 2020), and Image Net1K (IN-1K) (Deng et al., 2009). Beyond these, we also experiment with several fine-grained image classification datasets, CUB (Wah et al., 2011), Cars (Krause et al., 2013), PET (Parkhi et al., 2012), and satellite images, Euro SAT (Helber et al., 2018). |
| Dataset Splits | No | The paper provides 'Training Data Size' and 'Test Data Size' in Table 5 for each dataset, but does not explicitly detail a separate 'validation' split size or the specific methodology for all three splits. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or cloud instance types) used for running experiments were mentioned in the paper. |
| Software Dependencies | Yes | We finetune Stable Diffusion 1.5 (SDv1.5) (Rombach et al., 2022) with Lo RA. |
| Experiment Setup | Yes | The stable diffusion generation parameters are specified in Tab. 6. We use text prompt mentioned in Sec. 3.2... The fine-tuning hyperparameters used are specified in Tab. 7. ...the training hyperparameters are specified in Tab. 8. |