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..
Mosaic Representation Learning for Self-supervised Visual Pre-training
Authors: Zhaoqing Wang, Ziyu Chen, Yaqian Li, Yandong Guo, Jun Yu, Mingming Gong, Tongliang Liu
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that our method improves the performance far greater than the multi-crop strategy on a series of downstream tasks, e.g., +7.4% and +4.9% than the multi-crop strategy on Image Net-1K with 1% label and 10% label, respectively. |
| Researcher Affiliation | Collaboration | Zhaoqing Wang1,4 Ziyu Chen4 Yaqian Li4 Yandong Guo4 Jun Yu3 Mingming Gong2, Tongliang Liu1, 1 Sydney AI Centre, The University of Sydney 2 The University of Melbourne 3 University of Science and Technology of China 4 OPPO Research Institute |
| Pseudocode | Yes | D PSEUDO-CODES OF MOSAIC AUGMENTATION STRATEGY Algorithm 1: Mosaic augmentation strategy |
| Open Source Code | Yes | Code is available at https://github.com/DerrickWang005/MosRep.git. |
| Open Datasets | Yes | Datasets We perform self-supervised pre-training on two datasets, one middle-scale and another large-scale: 1) 100-category Image Net (IN-100) (Tian et al., 2019), a subset of IN-1K dataset containing 125k images; and 2) 1000-category Image Net (IN-1K) (Deng et al., 2009), the standard Image Net training set containing 1.25M images. |
| Dataset Splits | Yes | We evaluate the pre-trained models on the task of classification with limited Image Net labels. The sizes of annotations are reduced to 1% and 10% on the IN-1K (Deng et al., 2009) training dataset, respectively. ... evaluated on COCO 2017 val set. ... evaluated on Cityscapes val set. |
| Hardware Specification | Yes | with a mini-batch size of 256 on 8 NVIDIA V100 GPU. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for ancillary software dependencies. |
| Experiment Setup | Yes | As for the Mo Co version, we pre-train the network on IN-100 and IN-1K for 400 and 200 epochs, respectively. SGD (Loshchilov & Hutter, 2016) optimizer with a cosine learning rate scheduler and lrbase = 0.3 is adopted, with a mini-batch size of 256 on 8 NVIDIA V100 GPU. We utilize a negative queue of 16,384 for IN-100, and 65,536 for IN-1K. The weight decay is 0.0001 and SGD momentum is 0.9. |