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Found 14 papers, 4 papers with code
The Last Mile to Supervised Performance: Semi-Supervised Domain Adaptation for Semantic Segmentation
To address this, we study the promising setting of Semi-Supervised Domain Adaptation (SSDA).
Learning to Remove Cuts in Integer Linear Programming
Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs).
Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations
In this work, we go further and study DDPMs trained on strictly separate subsets of the data distribution with large gaps on the support of the latent factors.
Federated Learning under Covariate Shifts with Generalization Guarantees
This paper addresses intra-client and inter-client covariate shifts in federated learning (FL) with a focus on the overall generalization performance.
The Spectral Bias of Polynomial Neural Networks
Inspired by such studies, we conduct a spectral analysis of the Neural Tangent Kernel (NTK) of PNNs.
Conditional Generation Using Polynomial Expansions
We exhibit how CoPE can be trivially augmented to accept an arbitrary number of input variables.
A Rate-Distortion Approach to Domain Generalization
Domain generalization deals with the difference in the distribution between the training and testing datasets, i. e., the domain shift problem, by extracting domain-invariant features.
Protect the weak: Class focused online learning for adversarial training
In this work, we identify that the focus on the average accuracy metric can create vulnerabilities to the "weakest" class.
Poly-NL: Linear Complexity Non-local Layers with Polynomials
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
Ranked #1 on
Face Detection
on WIDER Face (Hard)
MVP: Multivariate polynomials for conditional generation
The conditional variable can be discrete (e. g., a class label) or continuous (e. g., an input image) resulting into class-conditional (image) generation and image-to-image translation models, respectively.
Poly-NL: Linear Complexity Non-Local Layers With 3rd Order Polynomials
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations
Deep generative models rely on their inductive bias to facilitate generalization, especially for problems with high dimensional data, like images.
Deep Polynomial Neural Networks
We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks.
Ranked #1 on
Face Recognition
on CALFW
PolyGAN: High-Order Polynomial Generators
Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions.
