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Found 12 papers, 1 papers with code
A Survey on Human-AI Teaming with Large Pre-Trained Models
In the rapidly evolving landscape of artificial intelligence (AI), the collaboration between human intelligence and AI systems, known as Human-AI (HAI) Teaming, has emerged as a cornerstone for advancing problem-solving and decision-making processes.
Tag-Based Annotation for Avatar Face Creation
Currently, digital avatars can be created manually using human images as reference.
Do humans and machines have the same eyes? Human-machine perceptual differences on image classification
Trained computer vision models are assumed to solve vision tasks by imitating human behavior learned from training labels.
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network
It delivers the class Relevance to the activated neurons in the intermediate layers in a back-propagation manner, and associates the activation of neurons with the input points to visualize the hidden semantics of each layer.
Tag-based annotation creates better avatars
Compared to direct annotation of labels, the proposed method: produces higher annotator agreements, causes machine learning to generates more consistent predictions, and only requires a marginal cost to add new rendering systems.
AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging
We propose a novel self-supervised learning framework to create high-quality stylized 3D avatars with a mix of continuous and discrete parameters.
How Much Does Input Data Type Impact Final Face Model Accuracy?
This idealized synthetic analysis is then compared to real results from several methods for constructing 3D faces from 2D photographs.
Can Graph Neural Networks Learn to Solve MaxSAT Problem?
With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning.
ε-weakened Robustness of Deep Neural Networks
This paper introduces a notation of $\varepsilon$-weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs).
Gated Transformer Networks for Multivariate Time Series Classification
In this work, we explored a simple extension of the current Transformer Networks with gating, named Gated Transformer Networks (GTN) for the multivariate time series classification problem.
DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training
In this paper, we introduce DuelGAN, a generative adversarial network (GAN) solution to improve the stability of the generated samples and to mitigate mode collapse.
Ranked #3 on
Image Generation
on Fashion-MNIST
