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• [CVPR 2022 Series #6] Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders

  Variational Autoencoder (VAE) [5,6] is one of the most popular deep latent variable models in modern machine learning. In VAE, we have a rich representational capacity to model a complex generative process of synthesizing images (x) from the latent variables (z).

• [CVPR 2022 Series #5] P>M>F: The Pre-Training, Meta-Training and Fine-Tuning Pipeline for Few-Shot Learning

  Many technical challenges must be overcome to provide excellent AI experience to users of Samsung devices. The Cambridge AI Center focuses on 'multimodal AI' technology, 'data efficient AI' and 'On-device AI technology that makes AI models work efficiently and robustly on Samsung's devices'. First, it is essential to handle multimodality for a natural interaction experience with users.

• [CVPR 2022 Series #4] Stereo Magnification with Multi-Layer Images

  Representing scenes with multiple semi-transparent colored layers has been a popular and successful choice for real-time novel view synthesis. Existing approaches infer colors and transparency values over regularly-spaced layers of planar or spherical shape.

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• SRC-B Wins Awards in Prominent AI Challenges at CVPR 2022

  The Conference on Computer Vision and Pattern Recognition (CVPR) is a world-renowned conference in the field of artificial intelligence (AI) and machine learning. Samsung R&D Institute China - Beijing (SRC-B) achieved excellent results in the New Trends in Image Restoration and Enhancement (NTIRE) workshop and Action Learning From Realistic Environments and Directives (ALFRED) workshop at CVPR 2022.

• [Blog] [CVPR 2022 Series #6] Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders

  Variational Autoencoder (VAE) [5,6] is one of the most popular deep latent variable models in modern machine learning. In VAE, we have a rich representational capacity to model a complex generative process of synthesizing images (x) from the latent variables (z).

• [Blog-CVPR 2022 Series #6] Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders

  Variational Autoencoder (VAE) [5,6] is one of the most popular deep latent variable models in modern machine learning. In VAE, we have a rich representational capacity to model a complex generative process of synthesizing images (x) from the latent variables (z).

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