Papers Style Generalization
“Style Generalization” 태그가 달린 논문 13편 · 필터 해제
MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation
Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains, but due to the absence of style diversity during training, enhancing performance on unseen target domains usi…
2D Semantic SegmentationAutonomous DrivingAutonomous VehiclesDomain Generalization+2GenerTTS: Pronunciation Disentanglement for Timbre and Style Generalization in Cross-Lingual Text-to-Speech
Cross-lingual timbre and style generalizable text-to-speech (TTS) aims to synthesize speech with a specific reference timbre or style that is never trained in the target language. It encounters the following challenges: …
DisentanglementStyle Generalizationtext-to-speechText to SpeechMega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias
Scaling text-to-speech to a large and wild dataset has been proven to be highly effective in achieving timbre and speech style generalization, particularly in zero-shot TTS. However, previous works usually encode speech …
AttributeInductive BiasLanguage ModelingLanguage Modelling+4Domain Generalization for Mammographic Image Analysis with Contrastive Learning
The deep learning technique has been shown to be effectively addressed several image analysis tasks in the computer-aided diagnosis scheme for mammography. The training of an efficacious deep learning model requires larg…
breast density classificationContrastive LearningDeep LearningDiversity+3Integral Probability Metrics PAC-Bayes Bounds
We present a PAC-Bayes-style generalization bound which enables the replacement of the KL-divergence with a variety of Integral Probability Metrics (IPM). We provide instances of this bound with the IPM being the total v…
Generalization BoundsStyle GeneralizationDiscrepancy-Optimal Meta-Learning for Domain Generalization
This work attempts to tackle the problem of domain generalization (DG) via learning to reduce domain shift with an episodic training procedure. In particular, we measure the domain shift with $\mathcal{Y}$-discrepancy an…
Bilevel OptimizationDomain GeneralizationMeta-LearningStyle GeneralizationA Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts
Wide usage of social media platforms has increased the risk of aggression, which results in mental stress and affects the lives of people negatively like psychological agony, fighting behavior, and disrespect to others. …
Aggression IdentificationCross-Lingual TransferDomain AdaptationStyle Generalization+1Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning
This paper presents a new semantic frame parsing model, based on Berkeley FrameNet, adapted to process spoken documents in order to perform information extraction from broadcast contents. Building upon previous work that…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Domain GeneralizationSemantic Frame Parsing+5Creative Flow+ Dataset
We present the Creative Flow+ Dataset, the first diverse multi-style artistic video dataset richly labeled with per-pixel optical flow, occlusions, correspondences, segmentation labels, normals, and depth. Our dataset in…
3D Character Animation From A Single PhotoDepth EstimationImage AnimationObject Tracking+5DLOW: Domain Flow for Adaptation and Generalization
In this work, we present a domain flow generation(DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to the other. The benefits of our DLOW mod…
Domain AdaptationSemantic SegmentationStyle GeneralizationNight-to-Day Image Translation for Retrieval-based Localization
Visual localization is a key step in many robotics pipelines, allowing the robot to (approximately) determine its position and orientation in the world. An efficient and scalable approach to visual localization is to use…
Image RetrievalPositionRetrievalStyle Generalization+2Complementary Attributes: A New Clue to Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen objects via sharing attributes. The generalization performance of ZSL is governed by the attributes, which transfer semantic information from …
AttributeStyle GeneralizationZero-Shot Learning(Almost) No Label No Cry
In Learning with Label Proportions (LLP), the objective is to learn a supervised classifier when, instead of labels, only label proportions for bags of observations are known. This setting has broad practical relevance, …
Generalization BoundsPrivacy PreservingStyle Generalization