Capitalised Terms in EVC-UFV V1.0 have the meaning defined in Table 1.
A dash “-” preceding a Term in Table 1 indicates the following readings according to the font:
- Normal font: the Term in the table without a dash and preceding the one with a dash should be read after that Term. For example, “Risk” and “- Assessment” will yield “Risk Assessment”.
- Italic font: the Term in the table without a dash and preceding the one with a dash should be read before that Term. For example, “Descriptor” and “- Financial” will yield “Financial Descriptors.”
All MPAI-specified Terms are defined online.
| Term | Definition |
| Block | |
| – Residual Block | A Block composed of concatenated DRLM modules where each module is followed by a Concatenation and Convolutional Layer. |
| Data Augmentation | A technique that increasing the training dataset with new training examples obtained by altering some features of the original training dataset. |
| Densely Residual Laplacian | |
| – Module | (DRLM) A set of RUs where each RU is followed by a Concatenation Layer. |
| – Network | |
| Dilation | |
| Dependency Graph | (DepGraph) a framework to simplify the Structured Pruning operation of neural networks. |
| Fine Tuning | |
| Frame | |
| – Video | |
| Inference | |
| Laplacian Attention Unit | (LC) A set of Convolutional Layers with a square filter size and Dilation that is greater than or equal the filter size. |
| Layer | |
| – Concatenation | |
| – Convolutional | |
| Model | |
| – Deep Learning | |
| – Machine Learning | |
| – Pre-trained | |
| Patch | |
| Parameter | |
| Patience | |
| Pre-training | |
| Pruning | The process of removing less important parameters (like weights or neurons) from a neural network to reduce its size and computational requirements, while retaining the model performance. |
| – Growing Regularization | A technique that forces the model to set a whole dimension to low values before applying the pruning, so as to make the removed dimension already close to zero not impacting the model result. |
| – Learning-Based | A set of Pruning techniques which require variations of learning in order to be implemented. |
| – Recovery | A method that involves retraining a pruned neural network to regain any lost accuracy. |
| – Structured | A method that removes entire components like neurons, filters, or channels, resulting in a smaller dense model architecture. |
| – Unstructured | Unstructured Pruning focuses on removing single redundant neurons. However, creating a sparse model representation which does not compute faster in common hardware. |
| Rectified Linear Unit | (ReLU) |
| Residual Unit | (RU) A set of alternate ReLU and Convolutional Layer. |
| Resolution | |
| Saliency Value | |
| Sampling | |
| – Down- | |
| – Up- | |
| Super Resolution | |
| Training |
