Notice: Function _load_textdomain_just_in_time was called incorrectly. Translation loading for the olympus-google-fonts domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/functions.php on line 6121 Warning: Cannot modify header information - headers already sent by (output started at /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/functions.php:6121) in /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/rest-api/class-wp-rest-server.php on line 1896 Warning: Cannot modify header information - headers already sent by (output started at /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/functions.php:6121) in /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/rest-api/class-wp-rest-server.php on line 1896 Warning: Cannot modify header information - headers already sent by (output started at /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/functions.php:6121) in /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/rest-api/class-wp-rest-server.php on line 1896 Warning: Cannot modify header information - headers already sent by (output started at /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/functions.php:6121) in /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/rest-api/class-wp-rest-server.php on line 1896 Warning: Cannot modify header information - headers already sent by (output started at /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/functions.php:6121) in /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/rest-api/class-wp-rest-server.php on line 1896 Warning: Cannot modify header information - headers already sent by (output started at /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/functions.php:6121) in /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/rest-api/class-wp-rest-server.php on line 1896 Warning: Cannot modify header information - headers already sent by (output started at /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/functions.php:6121) in /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/rest-api/class-wp-rest-server.php on line 1896 Warning: Cannot modify header information - headers already sent by (output started at /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/functions.php:6121) in /customers/2/b/9/gnereus.com/httpd.www/x2024/wp-includes/rest-api/class-wp-rest-server.php on line 1896 {"id":275,"date":"2024-07-30T09:27:39","date_gmt":"2024-07-30T09:27:39","guid":{"rendered":"https:\/\/gnereus.com\/x2024\/?p=275"},"modified":"2024-07-30T09:27:39","modified_gmt":"2024-07-30T09:27:39","slug":"generative-ai-constraints","status":"publish","type":"post","link":"https:\/\/gnereus.com\/x2024\/2024\/07\/30\/generative-ai-constraints\/","title":{"rendered":"Generative AI – Constraints"},"content":{"rendered":"\n

In generative models, constraining latent space can improve the quality and diversity of generated outputs. Constraints can be applied to ensure the latent space captures meaningful variations and adheres to desired properties.<\/p>\n\n\n\n

Types of Constraints<\/h2>\n\n\n\n
    \n
  1. Geometric Constraints:<\/strong> Ensuring the latent space has a specific geometric structure, such as a sphere or a manifold.<\/li>\n\n\n\n
  2. Regularization Techniques:<\/strong> Applying regularization methods like KL-divergence in VAEs to enforce a smooth, continuous latent space.<\/li>\n\n\n\n
  3. Supervised Constraints:<\/strong> Using labeled data to guide the structure of latent space, ensuring it captures relevant features for specific tasks.<\/li>\n<\/ol>\n\n\n\n

    Example: Applying Regularization<\/strong> In VAEs, a common constraint is the KL-divergence loss, which ensures the latent space follows a standard normal distribution.<\/p>\n\n\n\n

    # VAE loss function with KL-divergence\ndef loss_function(recon_x, x, mu, logvar):\n    BCE = nn.functional.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum')\n    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n    return BCE + KLD\n<\/code><\/code><\/pre>\n\n\n\n
    Supervised Constraints Example<\/strong> For GANs, using auxiliary classifiers to guide the latent space can help generate more controlled and diverse outputs.<\/p>\n\n\n\n
    # Example of a conditional GAN with supervised constraints\nclass Generator(nn.Module):\n    def __init__(self, latent_dim, n_classes, img_shape):\n        super(Generator, self).__init__()\n        self.label_emb = nn.Embedding(n_classes, latent_dim)\n        self.init_size = img_shape \/\/ 4\n        self.l1 = nn.Sequential(nn.Linear(latent_dim, 128 * self.init_size ** 2))\n\n    def forward(self, noise, labels):\n        gen_input = torch.mul(self.label_emb(labels), noise)\n        out = self.l1(gen_input)\n        out = out.view(out.shape[0], 128, self.init_size, self.init_size)\n        return out\n<\/code><\/code><\/pre>\n\n\n\n
    Applying constraints to latent space helps generative models produce more realistic and varied outputs. By carefully designing these constraints, we can improve model performance and achieve specific goals in data generation.<\/p>\n","protected":false},"excerpt":{"rendered":"
    In generative models, constraining latent space can improve the quality and diversity of generated outputs. Constraints can be applied to ensure the latent space captures meaningful variations and adheres to desired properties. Types of Constraints Example: Applying Regularization In VAEs, a common constraint is the KL-divergence loss, which ensures the latent space follows a standard […]<\/p>\n","protected":false},"author":1,"featured_media":270,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8,19,26],"tags":[4,20,27],"class_list":["post-275","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-computer-vision","category-generative-ai","tag-ai","tag-computer-vision","tag-generative-ai"],"_links":{"self":[{"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/posts\/275","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/comments?post=275"}],"version-history":[{"count":1,"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/posts\/275\/revisions"}],"predecessor-version":[{"id":276,"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/posts\/275\/revisions\/276"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/media\/270"}],"wp:attachment":[{"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/media?parent=275"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/categories?post=275"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gnereus.com\/x2024\/wp-json\/wp\/v2\/tags?post=275"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}