NNDL Unit 5
NNDL Unit 5
NNDL Unit 5
Working
Object detection
Image Classification:
Object Localization:
Task: The model not only classifies the object but also draws bounding
boxes around it.
Object Detection:
Task: The model predicts bounding boxes and assigns class labels to
each detected object.
Generator
Generator plays a central role in creating realistic and high-quality
images from random noise or latent vectors. The generator is a deep
neural network designed to map latent space representations to image
space, effectively generating images that mimic the distribution of the
training data. At its core, the generator aims to learn a mapping
function that transforms input noise vectors sampled from a latent
space into visually plausible images. This process typically involves
multiple layers of convolutional, upsampling, and activation functions,
enabling the generator to capture complex patterns and structures
present in the training data. During training, the generator receives
random noise vectors as input and generates corresponding images.
These generated images are then compared to real images from the
training dataset by the discriminator, another neural network
component in the GAN architecture. The discriminator's objective is to
distinguish between real and generated images, providing feedback to
both the generator and itself in an adversarial manner.Through this
adversarial training process, the generator learns to generate images
that are increasingly indistinguishable from real images, effectively
capturing the underlying distribution of the training data. As training
progresses, the generator refines its parameters to produce images with
higher fidelity, realism, and diversity. One of the key challenges in
training the generator is achieving a balance between generating
diverse and realistic images while avoiding mode collapse, where the
generator produces limited variations of the same image. Techniques
such as minibatch discrimination, feature matching, and spectral
normalization are commonly employed to mitigate mode collapse and
stabilize training. Overall, the generator in image generation with
GANs plays a crucial role in synthesizing novel and visually appealing
images from random noise inputs. By learning to capture the
underlying distribution of the training data, the generator enables the
GAN to generate images that exhibit realistic textures, structures, and
visual characteristics, opening up new possibilities for creative
expression, data augmentation, and image synthesis in various
domains.
Discriminator