{"id":13574,"date":"2025-07-29T04:11:16","date_gmt":"2025-07-29T04:11:16","guid":{"rendered":"https:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-precise-event-spotting-in-sports-videos-solving-long-range-dependency-and-class-imbalance-copy\/"},"modified":"2025-09-05T06:40:51","modified_gmt":"2025-09-05T06:40:51","slug":"summarizing-kanite-kolmogorov-arnold-networks-for-ite-estimation","status":"publish","type":"post","link":"https:\/\/whiteriversmediasolutions.com\/Sony\/summarizing-kanite-kolmogorov-arnold-networks-for-ite-estimation\/","title":{"rendered":"Summarizing \u2018KANITE: Kolmogorov-Arnold Networks for ITE Estimation\u2019"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"13574\" class=\"elementor elementor-13574\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cd44eb5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cd44eb5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9f11b70\" data-id=\"9f11b70\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-215a70e elementor-widget elementor-widget-heading\" data-id=\"215a70e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">BLOGS<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-28dc161 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"28dc161\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-63cf269\" data-id=\"63cf269\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6837436 elementor-widget elementor-widget-heading\" data-id=\"6837436\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Summarizing \u2018KANITE: Kolmogorov-Arnold Networks for ITE Estimation\u2019<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9bd1630 elementor-widget elementor-widget-text-editor\" data-id=\"9bd1630\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAbhinav Thorat, Ravi Kolla, Niranjan Pedanekar\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a034cb elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"7a034cb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>30<sup>th<\/sup> September 2024<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7d1e72 elementor-widget elementor-widget-image\" data-id=\"a7d1e72\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"750\" height=\"449\" src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture1-768x460.png\" class=\"attachment-medium_large size-medium_large wp-image-13577\" alt=\"\" srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture1-768x460.png 768w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture1-300x180.png 300w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture1.png 959w\" sizes=\"(max-width: 750px) 100vw, 750px\" style=\"width:100%;height:59.96%;max-width:959px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ed0fea elementor-widget elementor-widget-text-editor\" data-id=\"7ed0fea\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\">Figure 1: Overview of the proposed KANITE architecture<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9202657 elementor-widget elementor-widget-text-editor\" data-id=\"9202657\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Ravi Kolla summarizes the paper titled \u201cKANITE: Kolmogorov-Arnold Networks for ITE estimation\u201d co-authored by Abhinav Thorat, Ravi Kolla and Niranjan Pedanekar, accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases \u2013 ECML PKDD 2025.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f0a3e28 elementor-widget elementor-widget-text-editor\" data-id=\"f0a3e28\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Introduction<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d95d9a3 elementor-widget elementor-widget-text-editor\" data-id=\"d95d9a3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In causal inference, the estimation of Individual Treatment Effects (ITEs) is a foundational problem, as it is crucial for understanding the impact of a treatment on an individual user and personalizing treatments. ITE estimation has applications across a wide range of domains including healthcare, education, e-commerce, entertainment, and the social sciences. Despite its wide range of applications, ITE estimation remains challenging due to the absence of ground truth data, confounding factors, and treatment assignment bias. To overcome these issues, we propose new ITE estimation algorithms built on the recently popularized Kolmogorov\u2013Arnold Networks.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1246231 elementor-widget elementor-widget-text-editor\" data-id=\"1246231\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In the below we summarize the key contributions of work:<\/p><ul><li><strong>KANs Meet Causal Inference for the First Time<\/strong><br \/>While Kolmogorov\u2013Arnold Networks (KANs) have recently gained attention for their expressive power, no prior work has explored their application in causal inference\u2014specifically inindividual treatment effect (ITE) estimation. Our work is the first to integrate KANs into this space.<\/li><li><strong>Introducing KANITE: A Modular Framework for ITE Estimation<\/strong><br \/>We proposeKANITE, a novel framework that combines KANs with\u00a0shared representation learning\u00a0for estimating ITEs. To ensure the learned representations are balanced across treatment groups, we introduce a\u00a0representation loss\u00a0based on either\u00a0Integral Probability Metrics (IPM)\u00a0or\u00a0Entropy Balancing (EB). KANITE includes\u00a0three algorithmic variants such as KANITE-Wass, KANITE-MMD, KANITE-EB.<\/li><li><strong>Making Entropy Balancing Work Beyond Binary Treatments<\/strong><br \/>Entropy balancing is a popular technique for achieving covariate balance in binary treatment settings in causal effect estimation. We extend this idea tomultiple treatment\u00a0setups using\u00a0Lagrangian duality theory, allowing for more robust estimation in complex real-world settings. This results in a novel algorithm that tightly integrates\u00a0KANs with a multi-treatment version of the entropy balancing loss.<\/li><li><strong>Outperforming Baselines Consistently<\/strong><br \/>We rigorously benchmark KANITE on widely used datasets such as IHDP, NEWS (with 2 to 16 treatments), ACIC-16, andTwins including both binary and multiple treatment settings. KANITE consistently outperforms all baselines, demonstrating its practical effectiveness.<\/li><li><strong>Analyzing KAN Hyperparameters<\/strong><br \/>Beyond performance, we dive into how\u00a0KAN-specific parameters\u2014like\u00a0grid resolution\u00a0and the\u00a0degree of spline activations\u2014influence ITE estimation. These insights not only help interpret model behavior but also guide practitioners on how to fine-tune KANs for causal inference problems.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-424bb7e elementor-widget elementor-widget-text-editor\" data-id=\"424bb7e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Proposed <\/strong><strong>Model: KANITE<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd9e70e elementor-widget elementor-widget-text-editor\" data-id=\"cd9e70e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Our proposed KANITE framework addresses the task of ITE estimation for multiple treatments by utilizing KANs as the backbone of its architecture. It consists of three main building blocks, briefly explained below.<\/p><p>\u00a0<\/p><ol><li><strong>Balanced Representation of Covariates:<\/strong> First, KANITE aims to learn a balanced covariate representation by replacing the conventional MLPs with the KANs, shown as Representation Network in Figure 2, enabling the model to learn latent representations of covariates balanced across all treatment groups.<\/li><li><strong>Treatment Head Networks:<\/strong> It consists of dedicated treatment head networks, where each treatment is modeled through a separate representation using KANs, allowing greater flexibility to capture the underlying distribution of treatment outcomes.<\/li><li><strong>Representation loss:<\/strong> Three different representation losses have been considered in the proposed set of algorithms under KANITE. First and second losses are Maximum Mean Discrepancy (MMD) and Wasserstein, based on the Integral Probability Metric (IPM), and the third one utilizes Entropy Balancing (EB) method [33] to learn weights that minimize the Jensen-Shannon divergence, asymptotically, between all pairs of treatment groups. These three losses result into three different algorithms named KANITEMMD, KANITE-Wass, KANITE-EB for ITE estimation.<\/li><\/ol><p>\u00a0<\/p><p>Below, we provide the training details of the KANITE algorithm.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8981a35 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"8981a35\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1172\" height=\"723\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture2.png\" class=\"attachment-full size-full wp-image-13578 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture2.png 1172w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture2-300x185.png 300w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture2-1024x632.png 1024w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture2-768x474.png 768w\" data-sizes=\"(max-width: 1172px) 100vw, 1172px\" style=\"--smush-placeholder-width: 1172px; --smush-placeholder-aspect-ratio: 1172\/723;width:100%;height:61.69%;max-width:1172px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-487414c elementor-widget elementor-widget-text-editor\" data-id=\"487414c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>Few Key Results<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a64086 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"4a64086\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1334\" height=\"486\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture3.png\" class=\"attachment-full size-full wp-image-13579 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture3.png 1334w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture3-300x109.png 300w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture3-1024x373.png 1024w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture3-768x280.png 768w\" data-sizes=\"(max-width: 1334px) 100vw, 1334px\" style=\"--smush-placeholder-width: 1334px; --smush-placeholder-aspect-ratio: 1334\/486;width:100%;height:36.43%;max-width:1334px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b564932 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"b564932\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1313\" height=\"517\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture4.png\" class=\"attachment-full size-full wp-image-13580 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture4.png 1313w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture4-300x118.png 300w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture4-1024x403.png 1024w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2025\/07\/Picture4-768x302.png 768w\" data-sizes=\"(max-width: 1313px) 100vw, 1313px\" style=\"--smush-placeholder-width: 1313px; --smush-placeholder-aspect-ratio: 1313\/517;width:100%;height:39.38%;max-width:1313px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d5416e1 elementor-widget elementor-widget-text-editor\" data-id=\"d5416e1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4><strong>Conclusion<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9aff2cc elementor-widget elementor-widget-text-editor\" data-id=\"9aff2cc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>KANITE is a state-of-the-art framework for ITE estimation that leverages shared representation learning using either IPM or Entropy Balancing. Unlike traditional MLP-based architectures, KANITE employs KANs as its backbone, enabling it to learn more accurate causal effect estimates. The framework introduces three algorithms\u2014KANITE-MMD, KANITE-Wass, and KANITE-EB\u2014each utilizing a different IPM or Entropy Balancing-based representation loss to ensure balanced covariate representations across treatment groups. Experimental results demonstrate that KANITE effectively handles multipletreatmentscenarios, outperforming all considered baselines. Furthermore, KANITE achieves superior parameter efficiency and faster convergence while maintaining strong counterfactual prediction capabilities.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-67d4f0b elementor-widget elementor-widget-text-editor\" data-id=\"67d4f0b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>To know more about Sony Research India\u2019s Research Publications, visit the \u2018Publications\u2019 section on our \u2018Open Innovation\u2019s page:\u00a0<a href=\"https:\/\/www.sonyresearchindia.com\/open-innovation\/\">Open Innovation with Sony R&amp;D \u2013 Sony Research India<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0362925 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"0362925\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In most of the cases, it has been found that Content Driven sessions outperform the time driven sessions. The results are obtained on 6 baselines: STAMP, NARM, GRU4Rec, CD-HRNN, Tr4Rec on datasets like Movielens (Movies), GoodRead Book, LastFM (Music), Amazon (e-commerce).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c0518a1 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c0518a1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-b15be70\" data-id=\"b15be70\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-55dd72b\" data-id=\"55dd72b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e06d72d elementor-widget elementor-widget-image\" data-id=\"e06d72d\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"322\" data-src=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2.png\" class=\"attachment-full size-full wp-image-11786 lazyload\" alt=\"\" data-srcset=\"https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2.png 512w, https:\/\/whiteriversmediasolutions.com\/Sony\/uvaftoap\/2024\/02\/19th-Cover-Image-2-300x189.png 300w\" data-sizes=\"(max-width: 512px) 100vw, 512px\" style=\"--smush-placeholder-width: 512px; --smush-placeholder-aspect-ratio: 512\/322;width:100%;height:62.89%;max-width:512px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-fd52b32\" data-id=\"fd52b32\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9b69060 elementor-hidden-desktop elementor-hidden-tablet elementor-hidden-mobile elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9b69060\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cfbe302\" data-id=\"cfbe302\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6d045fb elementor-widget elementor-widget-text-editor\" data-id=\"6d045fb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe introduced modules and techniques help the proposed method to align known class\nrepresentations effectively so that it can detect the unknown objects accurately. To validate\nthis, we carried out extensive experiments &#038; ablation studies and found that the proposed\nmethod outperforms existing SOTA methods with significant improvement on the MS-COCO\n&#038; PASCAL VOC dataset for the OSOD task.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f97c4c4 elementor-widget elementor-widget-text-editor\" data-id=\"f97c4c4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTo know more about the paper, visit: <a href=\"https:\/\/openaccess.thecvf.com\/content\/WACV2024\/papers\/Sarkar_Open-Set_Object_Detection_by_Aligning_Known_Class_Representations_WACV_2024_paper.pdf\" target=\"_blank\" rel=\"noopener\">Open-Set Object Detection by Aligning Known Class\nRepresentations (thecvf.com)<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9e2f9cc elementor-widget elementor-widget-text-editor\" data-id=\"9e2f9cc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTo know more about Sony Research India\u2019s Research Publications, visit the \u2018Publications\u2019\nsection on our \u2018Open Innovation\u2019s page: <a href=\"https:\/\/www.sonyresearchindia.com\/open-innovation\/\" target=\"_blank\" rel=\"noopener\">Open Innovation with Sony R&amp;D \u2013 Sony Research\nIndia<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Ravi Kolla summarizes the paper titled \u201cKANITE&#8230; <\/p>\n","protected":false},"author":1,"featured_media":13577,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[22,17],"tags":[],"class_list":["post-13574","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all-blogs","category-technology","entry"],"yoast_head":"\n<title>Summarizing \u2018KANITE: Kolmogorov-Arnold Networks for ITE Estimation\u2019 - 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