Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression
In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space. Recently, Operational Neural Networks (ONNs) that learn the best non-linearity from a set of alternatives, and their self-organized variants, Self-ONNs, that approximate any non-linearity via Taylor series have been proposed to address the limitations of convolutional layers and a fixed nonlinear activation. In this paper, we propose to replace the convolutional and GDN layers in the variational autoencoder with self-organized operational layers, and propose a novel self-organized variational autoencoder (Self-VAE) architecture that benefits from stronger non-linearity. The experimental results demonstrate that the proposed Self-VAE yields improvements in both rate-distortion performance and perceptual image quality.
Code (0)
등록된 구현이 없습니다.
Tasks
Image CompressionSimilar Papers 제목 키워드 기반
Seeing the whole picture instead of a single point: Self-supervised likelihood learning for deep generative models
Recent findings show that deep generative models can judge out-of-distribution samples as more likely than those drawn from the same distribution as the training data. In this work, we focus on variational autoencoders (…
AAVAE: Augmentation-Augmented Variational Autoencoders
Recent methods for self-supervised learning can be grouped into two paradigms: contrastive and non-contrastive approaches. Their success can largely be attributed to data augmentation pipelines which generate multiple vi…
Contrastive LearningData Augmentationimage-classificationImage Classification+1Class Incremental Continual Learning with Self-Organizing Maps and Variational Autoencoders Using Synthetic Replay
This work introduces a novel generative continual learning framework based on self-organizing maps (SOMs) and variational autoencoders (VAEs) to enable memory-efficient replay, eliminating the need to store raw data samp…
Continual LearningSemi-Amortized Variational Autoencoders
Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network. While AVI has enabled efficient training of deep generative models such as variational autoencoders (VAE),…
Text GenerationVariational InferenceLearning a Driving Simulator
Comma.ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road. This paper illustrates one of o…
Self-Driving CarsVideo Prediction