CY Xu cy-xu

Analyzing and Improving the Image Quality of styleGAN

original StyleGAN was special as it maps input code z to an intermediate latent code w, which applied to AdaIN layers
stochastic variation helps the intermediate latent space W to be less entangled
this paper investigates and fixes:
- a. a droplet artifact in original StyleGAN paper via a redesigned norm in generator
- b. artifacts caused by progressive growing design via a mixture of skip-connection and residual nets
FID and P&R are useful but both based on classifier nets that shown to focus on textures rather than shapes
PPL metric correlates with consistency and stability of shapes, this is expensive so texecuted less frequently

	# Owning binary : TextComposerRuntime
	# Status : NEW in iOS 27 (not in 26.5.1)
	# Source : embedded __cstring in dyld_shared_cache_arm64e (24A5355q)
	======================================================================

	# Task Overview:
	You are a composition agent that helps users create personalized written content (emails, messages, documents, posts, stories, etc.)

	As an Assistant, you must:
	1. Analyze the request to determine if you have sufficient information

	"""
	Find the pattern and complete the function:
	int[][] spiral(int n);
	where n is the size of the 2D array.

	input = 3
	123
	894
	765

	""" Remove nonsense from a long list of emails
	hello.xu@gmail.com
	"""

	raw_emails_file = './email_addresses.txt'
	clean_email_file = './clean_email_addresses.txt'

	raw_emails = open(raw_emails_file, 'r').readlines()
	clean_emails = open(clean_email_file, 'w')

	import pdb
	import glob
	import astrodata

	# import dragon packages
	from recipe_system import cal_service
	from recipe_system.reduction.coreReduce import Reduce

	from gempy.utils import logutils
	from gempy.adlibrary import dataselect