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..
OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression
Authors: Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci, Zhenguo Li
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that vanilla OSOA can save significant time versus training bespoke models and space versus using one model for all targets. |
| Researcher Affiliation | Industry | Chen Zhang Shifeng Zhang Fabio M. Carlucci Zhenguo Li Huawei Noah s Ark Lab EMAIL |
| Pseudocode | Yes | Algorithm 1 One Shot Online Adaptation: Encoding and Decoding and Algorithm 2 The encode_or_cache method in OSOA Encoding |
| Open Source Code | No | The paper does not contain an explicit statement or link providing access to open-source code for the described methodology. |
| Open Datasets | Yes | The datasets for base model pretraining are the renowned natural image datasets CIFAR10 [28] and Image Net32 [7], including images of size 32 32. We obtain three target datasets randomly sampled from the large image dataset Yahoo Flickr Creative Commons 100 Million (YFCC100m) [46] to test the compression performance. |
| Dataset Splits | Yes | The data splitting strategy is the same as Stage 2. For Fine Tune v1, we fine tune the pretrained model for 2 epochs... For Fine Tune v2, we fine tune the pretrained model for 4 epochs for Hi LLo C (and IAF RVAE) and 3 epochs for IDF++... For Fine Tune v3, we fine tune the pretrained model for 20 epochs... We quadruple the batch size as the image size decreases, i.e., batch size 256/64/16 in Hi LLo C and batch size 48/12/3 in IDF++, for SET32/64/128 respectively. |
| Hardware Specification | Yes | We use an Nvidia V100 32GB GPU for Hi LLo C (and IAF RVAE) and an Nvidia V100 16 GB GPU for IDF++. |
| Software Dependencies | Yes | The time ratio we measured with/without the determinism is 1.98 (Hi LLo C) in Tensor Flow 1.14 [4] with tensorflow-determinism 0.3.0 [3] and 1.34 (IDF++) in Pytorch 1.6 [2]. |
| Experiment Setup | Yes | For Fine Tune v1, we fine tune the pretrained model for 2 epochs, as the whole OSOA Encoding & Decoding procedures involve 2 epochs of adaptations in total. For Fine Tune v2, we fine tune the pretrained model for 4 epochs for Hi LLo C (and IAF RVAE) and 3 epochs for IDF++... For Fine Tune v3, we fine tune the pretrained model for 20 epochs... We quadruple the batch size as the image size decreases, i.e., batch size 256/64/16 in Hi LLo C and batch size 48/12/3 in IDF++, for SET32/64/128 respectively. |