Let's denote an input image as $I \in \mathbb{R}^{H \times W \times C}$. The proposed deep feature, $\phi(I)$, is defined as: Free Nepali Sex Videos Extra Quality Apr 2026
The fractal analysis module, $\mathcal{M}$, uses the box-counting algorithm to estimate the fractal dimension of each augmented view at different scales. The fractal dimension is a measure of the self-similarity of the image at different scales. Template Ktp Kosong Photoshop Top [OFFICIAL]
Here's a potential deep feature:
The self-supervised learning module, $\mathcal{S}$, uses a contrastive loss function to learn a representation that is invariant to different augmentations of the input image.
The multi-scale representation is obtained by computing the fractal dimensions at different scales and concatenating them to form a single feature vector.
$$\phi(I) = \mathcal{F} \left( \mathcal{M} \left( \mathcal{S} \left( I \right) \right) \right)$$