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..
Explaining Self-Supervised Image Representations with Visual Probing
Authors: Dominika Basaj, Witold Oleszkiewicz, Igor Sieradzki, Michał Górszczak, Barbara Rychalska, Tomasz Trzcinski, Bartosz Zieliński
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Tab. 1 summarizes the results obtained in our experiments. |
| Researcher Affiliation | Collaboration | Dominika Basaj1,2 , Witold Oleszkiewicz1 , Igor Sieradzki3 , Michał G orszczak3 , Barbara Rychalska1,4 , Tomasz Trzci nski1,2,3 and Bartosz Zieli nski3,5 1Warsaw University of Technology 2Tooploox 3Faculty of Mathematics and Computer Science, Jagiellonian University 4Synerise 5Ardigen |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is at: github.com/Bio NN-Info Tech/visual-probes |
| Open Datasets | Yes | We conduct all of our experiments on the Image Net dataset [Deng et al., 2009] |
| Dataset Splits | Yes | We conduct all of our experiments on the Image Net dataset [Deng et al., 2009], keeping its standard train/validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions using a 'logistic regression classifier' and 'LBFGS solver' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We use a logistic regression classifier with a maximum of 1000 iterations and the LBFGS solver to train all diagnostic classifiers. ... We train 100 classifiers corresponding to 100 visual words. ... we group the possible output into 5 equally-wide bins... A similar procedure is applied to the character bin probing task, except that we use 6 bins in this case. ...we apply the random over-sampling if needed to deal with the imbalanced classes. |