This approach combines features from different network layers or resolutions for richer representation.
Depth features are integrated directly into standard feature maps, helping the network understand structure. With/In
Used to understand what a network perceives by detecting cluster structures in feature space. With/In
Increases detail representation and allows the model to leverage both low-level (texture) and high-level (semantic) information. 4. Deep Feature Factorization (DFF) With/In
(e.g., using toolkits like Alteryx)?
Reduces intra-class variance without significant computational overhead, making data points from the same class closer in the feature space. 2. Depth Awareness and Learnable Feature Fusion This technique embeds 3D geometry directly into CNNs.