#adversarial training
13 resultsMulti-level Domain Adaptive learning for Cross-Domain Detection
Rongchang Xie, Fei Yu, Jiachao Wang +2
The paper introduces a multi-level domain adaptive model that aligns both local and global feature distributions to improve object detection performance across different domains.
Towards Generating Stylized Image Captions via Adversarial Training
Omid Mohamad Nezami, Mark Dras, Stephen Wan +2
The paper presents ATTEND-GAN, an image captioning model that combines attention mechanisms with adversarial training to produce captions that are both content‑accurate and stylist…
Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization
Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng +4
The paper proposes a weakly‑supervised framework for temporal action localization that alternates between a Seeded Sequence Growing network, which expands reliable seed regions, an…
I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation
Laurens Samson, Nanne van Noord, Olaf Booij +3
The paper introduces a new adversarial training scheme for semantic segmentation where a "gambler" network bets on regions where the segmenter makes clear mistakes, encouraging the…
Robust Learning with Jacobian Regularization
Judy Hoffman, Daniel A. Roberts, Sho Yaida
The paper proposes an efficient Jacobian regularization method for neural networks that enlarges classification margins and improves robustness to both random and adversarial input…
An End-to-End Text-independent Speaker Verification Framework with a Keyword Adversarial Network
Sungrack Yun, Janghoon Cho, Jungyun Eum +2
The paper proposes an end-to-end speaker verification system that jointly trains a speaker embedding network and an automatic speech recognition network, using triplet loss and adv…
Adversarially Trained End-to-end Korean Singing Voice Synthesis System
Juheon Lee, Hyeong-Seok Choi, Chang-Bin Jeon +2
The paper introduces an end-to-end Korean singing voice synthesis system that converts lyrics and a symbolic melody into realistic singing audio using phonetic enhancement masking,…
Semi-Supervised Adversarial Monocular Depth Estimation
Rongrong Ji, Ke Li, Yan Wang +6
The paper introduces a semi‑supervised adversarial framework that uses a small set of labeled image‑depth pairs together with many unlabeled monocular images to improve monocular d…
Unsupervised Domain-Specific Deblurring via Disentangled Representations
Boyu Lu, Jun-Cheng Chen, Rama Chellappa
The paper proposes an unsupervised method for domain‑specific single‑image deblurring that separates content and blur features using disentangled encoders and leverages cycle‑consi…
Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging
Rachana Sathish, Ronnie Rajan, Anusha Vupputuri +2
The paper proposes an adversarially trained convolutional neural network to automatically segment core and penumbra regions of ischemic stroke lesions from multisequence MRI (DWI a…
Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images
Valentin Gabeur, Jean-Sebastien Franco, Xavier Martin +2
The paper introduces a non‑parametric method that reconstructs full 3D human shape from a single RGB image by predicting a visible and a hidden depth map, which are combined like a…
Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks
Jianyu Wang, Haichao Zhang
The paper introduces Bilateral Adversarial Training, which jointly perturbs images and labels during training using a one-step targeted attack and a heuristic adversarial label, en…
Joint Adversarial Training: Incorporating both Spatial and Pixel Attacks
Haichao Zhang, Jianyu Wang
The paper proposes a joint adversarial training approach that combines spatial transformation attacks with traditional pixel‑wise attacks to improve the robustness of deep vision m…