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..
DINO as a von Mises-Fisher mixture model
Authors: Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENTSWe conducted ablation experiments to study the impact of our proposed modifications to DINO. |
| Researcher Affiliation | Collaboration | 1Linköping University, Sweden 2 Qualcomm Technologies, Inc. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | We intend to make a public release of the code repository and pre-trained models in order to aid the research community to reproduce our experiments. |
| Open Datasets | Yes | The models are pre-trained on the Image Net dataset (Deng et al., 2009) |
| Dataset Splits | Yes | We report the k NN top-1 classification accuracy on Image Net in Table 1 by averaging over 2 runs. In Table 12 and Table 13, we show the mean and standard deviation of the top-1 validation classification accuracy over the three splits. |
| Hardware Specification | Yes | The model trainings are done on a single A100 node, consisting of 8 GPUs. |
| Software Dependencies | No | The paper mentions using the 'scipy' implementation for comparison, but no specific version numbers for any software dependencies are provided. |
| Experiment Setup | Yes | The student temperature τ is set to 0.1 and the teacher temperature is linearly scaled from 0.04 to 0.07 over some initial epochs (50 epochs for Vi T-Small/16 and 30 epochs for Vi T-Base/16). The batch sizes are adapted to fit the node and adjusted based on the model architecture (batch size=64 per GPU for Vi T-Base/16 and 128 for Vi T-Small/16). |