Adapting New Categories for Food Recognition with Deep Representation
Bibliographic record
Abstract
Learning to classify new (target) data in a different domain is always an interesting and challenging task in data mining. The classifier could suffer the dataset bias when predicting the new categories from target domain. Many adaptation methods have been proposed to adjust this bias but are limited to using data either from similar categories or requiring a large number of labeled examples from the target domain. Automatically adapting and recognizing new food categories is a very practical task in daily life. In this paper, we propose a new method that can alleviate the dataset bias for food image recognition. To obtain less biased feature representation from the food images, we fine-tuned GoogLeNet as our deep feature extractor and achieve state-of-the-art performance on the Food-101 dataset. Using the deep representation, our method can learn efficient classifiers with fewer labeled examples. More specifically, our method employs an external classifier for adaptation, called "negative classifier".Experiment results show that utilizing the parameters of the negative classifier, our method can achieve better performance and converge faster to adapt the new categories.
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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 | 0.001 |
| 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 | 0.000 |
| 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 | 0.001 |
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".