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..
Rejuvenating image-GPT as Strong Visual Representation Learners
Authors: Sucheng Ren, Zeyu Wang, Hongru Zhu, Junfei Xiao, Alan Yuille, Cihang Xie
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments showcase that D-i GPT excels as a strong learner of visual representations: A notable achievement is its compelling performance on the Image Net-1K dataset by training on publicly available datasets, D-i GPT unprecedentedly achieves 90.0% top-1 accuracy with a vanilla Vi TH. Additionally, D-i GPT shows strong generalization on the downstream task. |
| Researcher Affiliation | Academia | *Equal contribution 1Johns Hopkins University 2UC Santa Cruz. Correspondence to: Cihang Xie <EMAIL>. |
| Pseudocode | No | The paper describes the model architecture and methodology in text and through equations, but it does not include any explicitly labeled pseudocode or algorithm blocks/figures. |
| Open Source Code | Yes | Code is available at https://github.com/Oliver Rensu/D-i GPT. |
| Open Datasets | Yes | With Image Net-1K as the sole pertaining dataset, our base-size model achieves an 86.2% top-1 classification accuracy... [...] When further scaling the pretraining to Image Net-21K dataset... [...] The model is then finetuned on the LAION-400M dataset (Schuhmann et al., 2021; 2022). |
| Dataset Splits | Yes | Following (He et al., 2022), we finetune pretrained models using the Image Net-1K training set, and test on the Image Net-1K validation set with the input size of 224 224. [...] For semantic segmentation, we evaluate D-i GPT using the ADE20K dataset (Zhou et al., 2019), which comprises 150 categories with 20,000 training images and 2,000 validation images. |
| Hardware Specification | No | This work is supported by ONR with N00014-23-1-2641, TPU Research Cloud (TRC) program and Google Cloud Research Credits program. |
| Software Dependencies | No | The paper mentions using the Adam W optimizer, but it does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or CUDA versions). |
| Experiment Setup | Yes | Implementation details. In our experiments, we use CLIP to provide semantic tokens. We pretrain, by default, all models on Image Net-1K dataset for 300 epochs. We set the batch size to 4096 and the peak learning rate to lr = 1.5e 4 batchsize/256. We adopt a cosine learning rate decay schedule with a warm-up period of 40 epochs, and utilize the Adam W (Loshchilov & Hutter, 2019) optimizer with a weight decay of 0.05. We use random resized cropping and random horizontal flipping, with the input size set to 224 224. When further scaling the pretraining to Image Net-21K dataset, all models undergo 150 epochs of pretraining with a warm-up stage of 5 epochs, a learning rate lr = 1.5e 3, and a batch size of 4096. |