Incremental Hash-Bit Learning for Semantic Image Retrieval in Nonstationary Environments
Why this work is in the frame
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Bibliographic record
Abstract
Images are uploaded to the Internet over time which makes concept drifting and distribution change in semantic classes unavoidable. Current hashing methods being trained using a given static database may not be suitable for nonstationary semantic image retrieval problems. Moreover, directly retraining a whole hash table to update knowledge coming from new arriving image data may not be efficient. Therefore, this paper proposes a new incremental hash-bit learning method. At the arrival of new data, hash bits are selected from both existing and newly trained hash bits by an iterative maximization of a 3-component objective function. This objective function is also used to weight selected hash bits to re-rank retrieved images for better semantic image retrieval results. The three components evaluate a hash bit in three different angles: 1) information preservation; 2) partition balancing; and 3) bit angular difference. The proposed method combines knowledge retained from previously trained hash bits and new semantic knowledge learned from the new data by training new hash bits. In comparison to table-based incremental hashing, the proposed method automatically adjusts the number of bits from old data and new data according to the concept drifting in the given data via the maximization of the objective function. Experimental results show that the proposed method outperforms existing stationary hashing methods, table-based incremental hashing, and online hashing methods in 15 different simulated nonstationary data environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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