Entropy-based Sampling for Streaming learning with Move-to-Data approach on Video
Bibliographic record
Abstract
The current paradigm of training deep neural networks relies on large, annotated and representative datasets. They assume a static world where the target domain does not change. However, in the real-world, data changes over time and is often available on the fly. Naive retraining on new data causes catastrophic forgetting and the network is unable to generalize on old data. Streaming learning is a type of incremental learning where networks learn sequentially and as soon as a sample is available from the data stream. Instead of training on every new sample, we propose an uncertainty based selection criteria to improve our previously proposed fast streaming learning method Move-to-Data (MTD), called Entropy-based MTD (EMTD). Besides, streaming learning methods have so far mostly used Convolutional Neural Networks (CNNs) but in recent times Vision Transformers (ViTs) have shown much better performances for many vision tasks. Therefore, we use ViT based Video Transformer to analyse MTD, EMTD and their gradient descent based "retargeting" steps. We have compared the performances of EMTD with MTD (w/wo retargeting) and a popular streaming learning method ExStream for the transformer. EMTD is able to outperform baseline MTD, and EMTD with retargeting achieves close results as ExStream and is ∼ 1.2 times faster.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".