AKOOL Research

Welcome to our research hub, where we showcase groundbreaking work in GenAI.

Featured Publications

An Energy-Based Prior for Generative Saliency

Authors: Jing Zhang, Jianwen Xie, Nick Barnes, Ping Li
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023

In this paper, we introduce a novel energy-based prior for generative saliency models. Our approach enhances the interpretability and performance of saliency detection by integrating energy-based techniques, leading to more accurate and reliable results in various applications.

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Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood

Authors: Yaxuan Zhu, Jianwen Xie, Ying Nian Wu, Ruiqi Gao
Conference: The Twelfth International Conference on Learning Representations (ICLR), 2024

This research presents an innovative method for learning energy-based models using cooperative diffusion recovery likelihood. Our approach leverages the strengths of cooperative learning and diffusion processes to improve the training efficiency and effectiveness of energy-based models.

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Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation

Authors: Weinan Song, Yaxuan Zhu, Lei He, Ying Nian Wu, Jianwen Xie
Archive: ArXiv, 2024

We propose a progressive energy-based cooperative learning framework for multi-domain image-to-image translation. This method addresses the challenges of domain adaptation and translation by progressively refining the learning process, resulting in superior performance across multiple domains.

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Latent Plan Transformer: Planning as Latent Variable Inference

Authors: Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, Ying Nian WuArchive: ArXiv, 2024

In this paper, we introduce the Latent Plan Transformer, a novel framework that treats planning as latent variable inference. Our approach combines the strengths of transformers and latent variable models to achieve robust and efficient planning in complex environments.

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CoopHash: Cooperative Learning of Multipurpose Descriptor and Contrastive Pair Generator via Variational MCMC Teaching for Supervised Image Hashing

Authors: Khoa Doan, Jianwen Xie, Yaxuan Zhu, Yang Zhao, Ping Li

CoopHash introduces a novel approach to supervised image hashing through cooperative learning. By leveraging Variational Markov Chain Monte Carlo (MCMC) teaching, it simultaneously optimizes a multipurpose descriptor and a contrastive pair generator. This innovative method enhances the efficiency and accuracy of image hashing, making it a significant advancement in the field.

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Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space

Authors: Peiyu Yu, Dinghuai Zhang, Hengzhi He, Xiaojian Ma, Ruiyao Miao, Yifan Lu, Yasi Zhang, Deqian Kong, Ruiqi Gao, Jianwen Xie, Guang Cheng, Ying Nian Wu

This paper introduces a novel black-box optimization method known as the Latent Energy-Based Odyssey. The approach focuses on expanded exploration within an energy-based latent space, enhancing the optimization process. By leveraging the latent space’s energy landscape, the method improves the efficiency and effectiveness of optimization tasks, making it a significant advancement in the field of machine learning and optimization.

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Our team consists of renowned researchers from prestigious institutions, working collaboratively to advance the field of artificial intelligence. We focus on developing innovative solutions that address real-world problems and contribute to the broader scientific community.

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