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Found 14 papers, 10 papers with code
C3: High-performance and low-complexity neural compression from a single image or video
On the UVG video benchmark, we match the RD performance of the Video Compression Transformer (Mentzer et al.), a well-established neural video codec, with less than 5k MACs/pixel for decoding.
Spatial Functa: Scaling Functa to ImageNet Classification and Generation
Neural fields, also known as implicit neural representations, have emerged as a powerful means to represent complex signals of various modalities.
COIN++: Neural Compression Across Modalities
Neural compression algorithms are typically based on autoencoders that require specialized encoder and decoder architectures for different data modalities.
From data to functa: Your data point is a function and you can treat it like one
A powerful continuous alternative is then to represent these measurements using an implicit neural representation, a neural function trained to output the appropriate measurement value for any input spatial location.
COIN: COmpression with Implicit Neural representations
We propose a new simple approach for image compression: instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the image.
Generative Models as Distributions of Functions
By treating data points as functions, we can abstract away from the specific type of data we train on and construct models that are agnostic to discretization.
LieTransformer: Equivariant self-attention for Lie Groups
Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency through principled parameter sharing.
STEER : Simple Temporal Regularization For Neural ODE
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive.
STEER: Simple Temporal Regularization For Neural ODEs
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive.
Equivariant Neural Rendering
We propose a framework for learning neural scene representations directly from images, without 3D supervision.
Augmented Neural ODEs
We show that Neural Ordinary Differential Equations (ODEs) learn representations that preserve the topology of the input space and prove that this implies the existence of functions Neural ODEs cannot represent.
Ranked #21 on
Image Classification
on MNIST
Probabilistic Semantic Inpainting with Pixel Constrained CNNs
Semantic inpainting is the task of inferring missing pixels in an image given surrounding pixels and high level image semantics.
Learning Disentangled Joint Continuous and Discrete Representations
We present a framework for learning disentangled and interpretable jointly continuous and discrete representations in an unsupervised manner.
Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks
An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations.
