While this is an impressive feat, in order to implement loss functions, a CNN needs to be given examples of correct output in the form of labeled training data. Typically CNNs benefit from transfer learning, which is a practice that involves amassing knowledge about a problem and applying it...
The most important difference between deep learning and traditional machine learning is its performance as the scale of data increases. When the data is small, deep learning algorithms don’t perform that well. This is because deep learning algorithms need a large amount of data to understand it ...
'Squared Difference' refers to the computation of the square of the pixel-wise differences between two images, such as the generated HR image and the ground truth image, which is commonly used in evaluating image quality metrics like Mean Squared Error (MSE). ...
[18] discussed the corresponding relationships between the high-level features learned by expression CNN and the facial action units (AUs). To address the problem of insufficient expression data, data augment and transfer learning are two common means, e.g., Lopes et al. [20] adopted a set ...
Firstly, the algorithm establishes two feature extraction branches for visible and infrared images, respectively, and introduces a differential aware attention module (DAAM) between the two branches. This module enables the network to gradually integrate complementary information in the feature extraction ...
In self- attention mechanism [15, 56], the relationship between the tokens is modeled by the similarity between the projected query-key pairs, yielding the attention score. Instead of point-wise linear projection, we utilize temporal difference convolutio...
and subtract weight sharing mode to improve the accuracy of forest cover change detection. (2) The self-inverse network is introduced to detect the change of forest increase by using the sample data set of forest decrease, which realizes the transfer learning of the sample data set and ...
However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-...
Edge detection is a crucial step in many computer vision tasks, and in recent years, models based on deep convolutional neural networks (CNNs) have achieved human-level performance in edge detection. However, we have observed that CNN-based methods rely on pre-trained backbone networks and gener...
KSD is the upper bound that identifies the difference between the cumulative probability of experience and the cumulative probability of target distribution at each data point. MMD is mainly used to measure the distance between the distributions of two different but related random variables. Therefore,...