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Found 15 papers, 5 papers with code
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
We propose a novel paradigm of training with a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks.
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Solving Image PDEs with a Shallow Network
Partial differential equations (PDEs) are typically used as models of physical processes but are also of great interest in PDE-based image processing.
Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings
Digital watermarking is widely used for copyright protection.
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study
As a case study, we focus on the design of the quantization tables in the JPEG compression standard.
GIFnets: Differentiable GIF Encoding Framework
DitherNet manipulates the input image to reduce color banding artifacts and provides an alternative to traditional dithering.
Better Compression with Deep Pre-Editing
In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits.
Distortion Agnostic Deep Watermarking
Watermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference from the original image.
Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network
The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors.
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting
We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN).
Seq2Slate: Re-ranking and Slate Optimization with RNNs
Ranking is a central task in machine learning and information retrieval.
Laplacian Smoothing Gradient Descent
We propose a class of very simple modifications of gradient descent and stochastic gradient descent.
Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time.
Constrained Classification and Ranking via Quantiles
The maximization of many of these metrics can be expressed as a constrained optimization problem, where the constraint is a function of the classifier's predictions.
Deep Neural Nets with Interpolating Function as Output Activation
We replace the output layer of deep neural nets, typically the softmax function, by a novel interpolating function.
Uncertainty quantification in graph-based classification of high dimensional data
In this paper we introduce, develop algorithms for, and investigate the properties of, a variety of Bayesian models for the task of binary classification; via the posterior distribution on the classification labels, these methods automatically give measures of uncertainty.
