Channel Attention Networks for Image Translation
Bibliographic record
Abstract
Existing image-to-image translation methods usually adopt an encoder-decoder structure to generate images. The encoder extracts the features of input images using a sequence of convolution layers until a bottleneck, and then, the intermediate features are decoded to the target image. However, the existence of bottleneck layer in such structure may lead to blurry and bad quality of the translated images, since different domain translations may be related to the global or local region in the input image or even in an abstract level. To prevent these problems, we propose the channel attention networks for image translation in this paper. It is a novel model that supports the multi-domain image-to-image translation using one single model. Conditioning on the target domain label, an auto-encoder-like network with multiple attention connections is trained to translate the input image into the target domain. The attention connections better shuttle the low-level information in the encoder to the decoder, which helps to preserve the structure. A multi-level attention mechanism is also designed in the proposed model to further improve the performance of our model. More specially, the feature maps in the encoder are first squeezed by average pooling and used to output a channel-wise attention mask. The attention mask softly determines which channels of the feature maps are translated and which channels are kept. By enforcing the model to learn a cyclic domain transformation during training, our model does not require paired training data, which greatly improves the versatility to different kinds of data. We experimentally demonstrated the effectiveness of our proposed model on the facial and clothing image translation tasks. The extensive ablations are also conducted to further validate the contribution of the proposed attention module used in our model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".