prakashjay prakashjayy

\begin{equation*}
\left[ \begin{array}{cccc}
i_{1} & i_{2} & i_{3} \\\end{array} \right] \times \left[ \begin{array}{cccc}
W_{i1j1} & W_{i1j2} & W_{i1j3} \\
W_{i2j1} & W_{i2j2} & W_{i2j3} \\
W_{i3j1} & W_{i3j2} & W_{i3j3} \\ \end{array} \right] = \left[ \begin{array}{cccc}
h_{1in} & h_{2in} & h_{3in} \\\end{array} \right]
\end{equation*}

These are the list of papers concepts you should learn to become a PRO in GAN Research. We will see one as a step by step improvment in generation quality, stabilized training etc.

The first paper on GAN by IAN GoodFellow and team. There are many blogs on how a GAN works based on this paper. A simple search on google should give u many results. Asking an LLM is far more better.
GANs more susceptible to mode-collapse, vanishing gradients, so people moved away from simple BCE loss to Earth mover distance and some kind of regalurization to avoid these. Read WGAN and WGAN-GP papers to understand this again.
Next up is ProGAN. this is the first paper to my knowledge which basically made GANs possible to generate high resolution images. though progressive training is out of fashion, I still consider one should read about this [https://arxiv.org/pdf/1710.10196v3]. Their another key contribution is

	from keras import applications
	from keras.preprocessing.image import ImageDataGenerator
	from keras import optimizers
	from keras.models import Sequential, Model
	from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
	from keras import backend as k
	from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping

	img_width, img_height = 256, 256
	train_data_dir = "data/train"

	from keras import applications
	from keras.preprocessing.image import ImageDataGenerator
	from keras import optimizers
	from keras.models import Sequential, Model
	from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
	from keras import backend as k
	from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping

	img_width, img_height = 256, 256

	# This is the docker image available. I am using cpu version here. If needed there is gpu version available.
	FROM bvlc/caffe:cpu

	# Copy the file into docker
	COPY requirements.txt requirements.txt

	# Run the copied file
	RUN pip install -r requirements.txt

	# create a folder called model1 and copy all the files in the folder into that folder

	import torch
	import torch.nn as nn
	import torch.optim as optim
	from torch.optim import lr_scheduler
	import numpy as np
	import time
	import os
	import argparse

	## Load the model

	# Fastscript usage under various instancees
	# https://github.com/fastai/fastscript/blob/master/00_core.ipynb
	# https://fastcore.fast.ai/script.html

	from fastcore.all import *

	@call_parse
	def test_script(p: Param(help="any basic string", type=str)):
	print(len(p))

	@patch_to(ds.FeatureCollection, cls_method=True)
	def read_generic(cls,
	fp: str,
	dst_crs: Union[str, rasterio.crs.CRS]=None,
	):
	'''
	geojson reader and can also be used to ensure the geojson in mapped to a destined crs
	'''
	with open(fp, 'r', encoding='utf-8') as f:
	collection = json.load(f)

	def get_tile_name_path(dst_dir, index, city=city, code=code):
	'''
	generating specific tile name
	'''
	dst_tile_name = "{}_{}_{}.tif".format(city, code, str(index).zfill(5))
	dst_tile_path = os.path.join(dst_dir, dst_tile_name)
	return dst_tile_name, dst_tile_path

	def get_tile_transform(parent_transform, pixel_x,pixel_y):
	'''