1. Draw the YOLO v4 complex Architecture. 2. Why is SIFT invariant to scale and Angle? 3. What is the difference between GAN's and VAE?

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1. YOLO v4 is a complex architecture for object detection. 2. SIFT is invariant to scale and angle due to its keypoint detection and descriptor extraction techniques. 3. GANs and VAEs differ in their approach to generative modeling.

YOLO v4 is a state-of-the-art object detection architecture that uses a deep neural network with multiple layers.
SIFT (Scale-Invariant Feature Transform) is invariant to scale and angle because it uses a scale-space extrema detection algorithm and keypoint descriptor extraction.
GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders) are both generative models, but they have different underlying principles.
GANs consist of a generator and a discriminator network that compete against each other to generate realistic data.
VAEs use an encoder-decoder architecture with a latent space to learn a probabilistic distribution of the input data.
GANs are known for generating high-quality samples but lack control over the generated output.
VAEs provide control over the generated output but may produce blurry samples.
Both GANs and VAEs have applications in image generation, anomaly detection, and data augmentation.

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1. Draw the YOLO v4 complex Architecture. 2. Why is SIFT invariant to scale and Angle? 3. What is the difference between GAN's and VAE?

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