An unsupervised learning based method for content-based image retrieval using hopfield neural network
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
Presently, corporations and individuals have large image databases due to the explosion of multimedia and storage devices available. Furthermore, the accessibility to high speed internet has escalated the level of multimedia exchanged by users across cyberspace every second. Accordingly, it has increased the demand for searching among large databases of images. Conventionally, text-based image retrieval is used. The major problems in text-based image retrieval are related to annotation that is often impossible due to human perception of images being subjective, and also due to the size of the information that needs indexing. To overcome such limitations, content-based image retrieval systems have been proposed. However, there is a key hindrance, namely, the need to match the human visual system to overcome the semantic gap between human perception and low-level features. In this paper, we propose a new unsupervised method based on Hopfield neural networks that seeks to model human visual memory to increase the efficacy of retrieval and reduce the semantic gap. A comparative study with other neural-network based methods, such as the feed forward backpropagation and Boltzmann deep learning, shows the effectiveness of our method.
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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 | 0.001 | 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 | 0.001 | 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