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Found 5 papers, 4 papers with code
Flexible Variable-Rate Image Feature Compression for Edge-Cloud Systems
By compressing different intermediate features of a pre-trained vision task model, the proposed method can scale the encoding complexity without changing the overall size of the model.
Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs
Recent studies reveal a significant theoretical link between variational autoencoders (VAEs) and rate-distortion theory, notably in utilizing VAEs to estimate the theoretical upper bound of the information rate-distortion function of images.
Probing Image Compression For Class-Incremental Learning
To this end, we introduce a new framework to incorporate image compression for continual ML including a pre-processing data compression step and an efficient compression rate/algorithm selection method.
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification
Our focus was on understanding the impact of the freezing mechanism on performance.
QARV: Quantization-Aware ResNet VAE for Lossy Image Compression
This paper addresses the problem of lossy image compression, a fundamental problem in image processing and information theory that is involved in many real-world applications.
