made suggested changes
"cell_type": "markdown",
"metadata": {},
"source": [
- "<center>Figure 1. Overview of CycleGAN architecture</center>"
+ "<center>Figure 1. Overview of CycleGAN architecture: Translating from satellite image to map routes domain</center>"
]
},
{
"\n",
"\n",
"1. Adversarial Loss: We apply Adversarial Loss to both the Generators, where the Generator tries to generate the images of it's domain, while its corresponding discriminator distinguishes between the translated samples and real samples. Generator aims to minimize this loss against its corresponding Discriminator that tries to maximize it.\n",
- "2. Cycle Consistency Loss: It captures the intuition that if we translate the image from one domain to the other and back again we should arrive at where we started. Hence, it calculates the L1 loss between the original image and the final generated image, which should look same as original image. It has 2 parts:\n",
+ "2. Cycle Consistency Loss: It captures the intuition that if we translate the image from one domain to the other and back again we should arrive at where we started. Hence, it calculates the L1 loss between the original image and the final generated image, which should look same as original image. It is calculated in two directions:\n",
" * Forward Cycle Consistency: Domain-B -> **Generator-A** -> Domain-A -> **Generator-B** -> Domain-B\n",
" * Backward Cycle Consistency: Domain-A -> **Generator-B** -> Domain-B -> **Generator-A** -> Domain-A\n",
"\n",
