Investigating the Interaction between Data and Algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research in computer vision is centered on algorithmic improvements, for example, by developing better models. Thereby, the data is considered fixed. This is in contrast to many real-world applications of computer vision systems in which algorithms and data co-evolve. To address this shortcoming of previous research, we study the properties of the data and their interaction with deep learning algorithms. Thereby, we investigate the size of the data, the share of mislabels, class imbalance and the presence of unlabeled data which can be leveraged using semi-supervised learning. In experiments on 100 classes from ImageNet, we show that a tiny network architecture outperforms a much more powerful one it if has access to only a little bit more data. Only if vast amounts of data are available so that adding even more images has little effect on performance, large architectures dominate smaller ones. If little data is provided, adding a few labeled images has a huge effect on accuracy. Once accuracy saturates, massive amounts of additional data are needed to achieve even small improvements. Furthermore, we find that mislabels severely reduce performance. To fix that, we propose a cost-efficient way of identifying mislabels which is especially beneficial if many images are already available. Conversely, if little data is available, labeling more images is more advantageous than cleaning existing annotations. In the case of imbalanced data, we illustrate that labeling more instances from rare classes has a much greater effect on performance than only increasing dataset size. Moreover, we show that leveraging unlabeled images by semi-supervised learning offers a consistent benefit even if the labeled subset contains significant label noise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it