Improving Adversarial Energy-Based Model via Diffusion Process
Authors: Cong Geng, Tian Han, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Søren Hauberg, Bo Li
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show significant improvement in generation compared to existing adversarial EBMs, while also providing a useful energy function for efficient density estimation. 4. Experiments We evaluate our DDAEBM in different scenarios across different data scales from 2-dimension toy datasets to large-scale image datasets. We test our energy function mainly on toy datasets and MNIST datasets which are easy to visualize and intuitive to measure. For large-scale datasets, we focus on image generation. We further perform some additional studies such as out-of-distribution (OOD) detection and ablation studies to verify our model s superiority. |
| Researcher Affiliation | Collaboration | 1vivo Mobile Communication Co., Ltd, China 2Department of Computer Science, Stevens Institute of Technology, USA 3Technical University of Denmark, Copenhagen, Denmark. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of open-source code or a direct link to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate our DDAEBM in different scenarios across different data scales from 2-dimension toy datasets to large-scale image datasets. We test our energy function mainly on toy datasets and MNIST datasets which are easy to visualize and intuitive to measure. For large-scale datasets, we focus on image generation. We further perform some additional studies such as out-of-distribution (OOD) detection and ablation studies to verify our model s superiority. ... training on 32 32 CIFAR-10 (Krizhevsky et al., 2009), 64 64 Celeb A (Liu et al., 2015), and 128 128 LSUN church (Yu et al., 2015) datasets. ... SVHN (Netzer et al., 2011), Texture (Cimpoi et al., 2014), CIFAR-100 (Krizhevsky et al., 2009) and Celeb A. |
| Dataset Splits | No | The paper mentions training epochs and test sets, but does not provide specific details about validation dataset splits (e.g., percentages, counts, or methodology for creation). |
| Hardware Specification | No | The paper mentions '4 GPUs' in Table 10 (Hyper-parameters for our training optimization), but it does not specify any particular GPU models, CPU models, memory details, or other specific hardware configurations used for running the experiments. |
| Software Dependencies | Yes | We use Pytorch 1.10.0 and CUDA 11.3 for training. |
| Experiment Setup | Yes | We specify the hyperparameters used for our generators and training optimization on each dataset in Table 9 and Table 10. Table 9. Hyper-parameters for our generator network (CIFAR-10, Celeb A, LSUN church, # of Res Net blocks per scale, Initial # of channels, Channel multiplier, Scale of attention block, Latent Dimension, # of latent mapping layers, Latent embedding dimension). Table 10. Hyper-parameters for our training optimization (MNIST, CIFAR-10, Celeb A, LSUN church, Initial learning rate, βmin, βmax in Eq. (47), w, wmid in Eq. (21), Adam optimizer β1, β2, EMA, Batch size, # of training epochs, # of GPUs). |