Adaptive IMLE for Few-shot Pretraining-free Generative Modelling
Authors: Mehran Aghabozorgi, Shichong Peng, Ke Li
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate on multiple few-shot image synthesis datasets that our method significantly outperforms existing methods. Our code is available at https: //github.com/mehranagh20/Ada IMLE. |
| Researcher Affiliation | Academia | 1APEX Lab, School of Computing Science, Simon Fraser University. Correspondence to: Mehran Aghabozorgi <mehran_aghabozorgi@sfu.ca>, Shichong Peng <shichong_peng@sfu.ca>, Ke Li <keli@sfu.ca>. |
| Pseudocode | Yes | Algorithm 1 Vanilla IMLE procedure |
| Open Source Code | Yes | Our code is available at https: //github.com/mehranagh20/Ada IMLE. |
| Open Datasets | Yes | We evaluate our method and the baselines on a wide range of natural image datasets at 256 256 resolution, which includes Animal-Face Dog and Cat (Si & Zhu, 2012), Obama, Panda, and Grumpy-cat (Zhao et al., 2020b) and Flickr-Face HQ (FFHQ) subset (Karras et al., 2019). |
| Dataset Splits | No | For LPIPS backtracking, we use 90% of the full dataset for training and evaluate the metric using the remaining 10% of the dataset. This describes a train/test split, but no separate validation split for hyperparameter tuning is explicitly mentioned. |
| Hardware Specification | Yes | We train our model for less than 200k iterations with a mini-batch size of 4 using the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 2 10 6 on a single NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions using Adam optimizer but does not specify versions of software libraries (e.g., PyTorch, TensorFlow, Python) or other specific ancillary software with version numbers. |
| Experiment Setup | Yes | Our network architecture is modified from (Child, 2021), where we keep the decoder architecture and replace the encoder with a fully-connected mapping network inspired by (Karras et al., 2019). We choose an input latent dimension of 1024, m = 10000 and a tightening coefficient δ = 0.98. We train our model for less than 200k iterations with a mini-batch size of 4 using the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 2 10 6 on a single NVIDIA V100 GPU. |