Hypernetworks, or hypernets for short, are neural networks that generate weights for another
neural network, known as the target network. They have emerged as a powerful deep …

Abstract Personalized Federated Learning (pFL) not only can capture the common priors
from broad range of distributed data, but also support customized models for heterogeneous …

Implicitly defined, continuous, differentiable signal representations parameterized by neural
networks have emerged as a powerful paradigm, offering many possible benefits over …

Personalized federated learning is tasked with training machine learning models for multiple
clients, each with its own data distribution. The goal is to train personalized models …

Blind image quality assessment (BIQA) for authentically distorted images has always been a
challenging problem, since images captured in the wild include varies contents and diverse …

We present a novel, real-time, semantic segmentation network in which the encoder both
encodes and generates the parameters (weights) of the decoder. Furthermore, to allow …

In most existing learning systems, images are typically viewed as 2D pixel arrays. However,
in another paradigm gaining popularity, a 2D image is represented as an implicit neural …

Geo-tagged images are publicly available in large quantities, whereas labels such as object
classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has …

Abstract Multi-Layer Perceptrons (MLPs) make powerful functional representations for
sampling and reconstruction problems involving low-dimensional signals like images …

Although deep networks are typically used to approximate functions over high dimensional
inputs, recent work has increased interest in neural networks as function approximators for …