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Found 11 papers, 2 papers with code
Learning to Cartoonize Using White-Box Cartoon Representations
This paper presents an approach for image cartoonization.
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia
Specifically, we disentangle the effects of Adaptive Learning Rate and Momentum of the Adam dynamics on saddle-point escaping and minima selection.
Generating Manga From Illustrations via Mimicking Manga Creation Workflow
To this end, we create a large-scale dataset with these three components annotated by artists in a human-in-the-loop manner.
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum
Specifically, we disentangle the effects of Adaptive Learning Rate and Momentum of the Adam dynamics on saddle-point escaping and flat minima selection.
Adaptive Federated Minimax Optimization with Lower Complexities
To fill this gap, in the paper, we study a class of nonconvex minimax optimization, and propose an efficient adaptive federated minimax optimization algorithm (i. e., AdaFGDA) to solve these distributed minimax problems.
Realtime Fewshot Portrait Stylization Based On Geometric Alignment
Previous learning based stylization methods suffer from the geometric and semantic gaps between portrait domain and style domain, which obstacles the style information to be correctly transferred to the portrait images, leading to poor stylization quality.
Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels
In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge.
Multi-modal Facial Action Unit Detection with Large Pre-trained Models for the 5th Competition on Affective Behavior Analysis in-the-wild
Facial action unit detection has emerged as an important task within facial expression analysis, aimed at detecting specific pre-defined, objective facial expressions, such as lip tightening and cheek raising.
Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning
Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise.
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models
Instruction tuning is instrumental in enabling Large Language Models~(LLMs) to follow user instructions to complete various open-domain tasks.
Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learning
This study explores a learning-based tri-finger robotic arm manipulating task, which requires complex movements and coordination among the fingers.
