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Explore multi-scale multi-band DenseNets for audio source separation, featuring innovative architecture with up-sampling, block skip connection, and band-dedicated dense blocks to improve performance and efficiency.
Explore 3D-aware image generation by modifying StyleGANv2 with multiplane alpha maps and pose-conditioned discrimination, achieving high-quality, view-consistent results across diverse datasets.
Explore video understanding with UniFormer, a novel approach addressing inefficiencies in video transformers for improved performance and pose estimation.
Explore generative facial priors for real-world blind face restoration, including GFP-GAN, degradation removal, and channel-split spatial feature transform techniques.
Explore nnFormer, a novel AI model architecture, its envelope, mass data handling, and experimental results in this comprehensive overview.
Explore a Transformer-based framework for multivariate time series analysis, covering challenges, model architecture, pre-training, fine-tuning, and applications in classification and forecasting.
Learn about DF-GAN, a deep fusion architecture for text-to-image synthesis. Explore its simplified backbone, gradient penalty, and deep fusion block, with quantitative and qualitative results.
Explore online personalized recommendations using Deep Bayesian Bandits. Learn about UCB, Thompson Sampling, and innovative approaches like bootstrapping and dropout methods for effective exploration strategies.
Learn to generate realistic lip movements from speech using advanced AI techniques, exploring models, evaluation methods, and real-world applications in this cutting-edge field.
Explore cutting-edge semi-supervised learning techniques using large self-supervised models, examining their potential and impact on machine learning research and applications.
Explore innovative techniques for improving speech recognition using reinforcement learning-based speech enhancement, covering key concepts and potential future advancements.
Explore advanced techniques for image segmentation using reinforced active learning, covering challenges, algorithms, and experimental results.
Interactive visualization tool for understanding convolutional neural networks, covering layers, filters, padding, activation, and pooling. Enhances learning through hands-on exploration.
Explore advanced image classification techniques using self-training and noisy student methods, improving accuracy and robustness in deep learning models.
Explore deep learning model vulnerabilities and defense strategies against adversarial attacks, examining research findings and experimental results.
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