Yes. Different sums are expected due to dropout, but dropout shouldn't change the values that are already there. Since we are dropping out a softmaxed tensor over dim=-1, we would expect that the value after dropout to still be between 0 and 1 and sum over dim=-1 to an amount smal...
Attention moduleFeature droppingFeature diversityOccluded person re-identificationAttention mechanism is widely employed in Person Re-Identification task to allocate the weight of features. However, most of the existing attention-based methods focus on the region of interest but ignore other potential ...
As shown in Table 5, the separate-type model has marked performance degradation on both tasks compared to a joint-type model, with F1-scores dropping by 2.87% for NER and 1.61% for RE. Experimental results prove the interdependencies between the type’s information of entities and relations, ...
The Laptop14 and Restaurant15 datasets showed particularly severe performance degradation, with the accuracy and F1 metrics dropping by 4.25% and 4.74%, respectively, for the Laptop14 dataset, and the F1 metric dropping by 4.04% for the Restaurant15 dataset, indicating that the interaction between ...
The results show significant improvements, with the RMSE dropping to 0.004223°, MAE to 0.003021°, ADE to 0.002436°, and FDE to 0.003946°. Compared to G9, the RMSE is reduced by 3.5%, and ADE by 4.7%, demonstrating that the gating mechanism helps the model better utilize spatial ...
Comparing this with the last row, there is a substantial decrease in model performance, with F1 scores dropping by 34.99% and 43.68% at the note level and frame level, respectively. Focal Loss demonstrates significant advantages in focusing the model’s attention on hard-to-classify samples, ...
The overall curve trend is favorable, with both the train loss and val loss dropping below 0.05 by the end of training. This demonstrates that the proposed GMDNet network effectively converges on the HGCrack dataset. Figure 10. The train loss curve and val loss curve obtained from GMDNet ...
Rajwade in [20] used singular value decomposition for image denoising; noises are considered to relate to smaller singular values, and the noises are removed by dropping smaller singular values. Sparse and redundant representation is another popular transform domain denoising method, which trains a ...