Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive IMLE for Few-shot Pretraining-free Generative Modelling
Authors: Mehran Aghabozorgi, Shichong Peng, Ke Li
ICML 2023 | Venue PDF | 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 <EMAIL>, Shichong Peng <EMAIL>, Ke Li <EMAIL>. |
| 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. |