Search Results for author:
Found 25 papers, 4 papers with code
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing
Since the generated samples simulate the low density area for each modal, the discriminator could directly detect anomalies from normal data.
Example-Guided Image Synthesis using Masked Spatial-Channel Attention and Self-Supervision
Example-guided image synthesis has recently been attempted to synthesize an image from a semantic label map and an exemplary image.
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game
To the best of our knowledge, these are the first computationally efficient and nearly minimax optimal algorithms for offline single-agent MDPs and MGs with linear function approximation.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders
Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis.
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
When the dataset does not have uniform coverage over all policy pairs, finding an approximate NE involves challenges in three aspects: (i) distributional shift between the behavior policy and the optimal policy, (ii) function approximation to handle large state space, and (iii) minimax optimization for equilibrium solving.
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
In this work, we break this barrier and study incentivized exploration with multiple and long-term strategic agents, who have more complicated behaviors that often appear in real-world applications.
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization
In this paper, we propose BEACON -- Batched Exploration with Adaptive COmmunicatioN -- that closes this gap.
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
To fully inherit the benefits of distributional RL and hybrid reward architectures, we introduce Multi-Dimensional Distributional DQN (MD3QN), which extends distributional RL to model the joint return distribution from multiple reward sources.
Distributional Reinforcement Learning
reinforcement-learning
GANet: Glyph-Attention Network for Few-Shot Font Generation
Font generation is a valuable but challenging task, it is time consuming and costly to design font libraries which cover all glyphs with various styles.
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search
We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction.
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction
This paper considers the decentralized composite optimization problem.
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
Relevance has significant impact on user experience and business profit for e-commerce search platform.
Image Sentiment Transfer
In this paper, we propose an effective and flexible framework that performs image sentiment transfer at the object level.
LinksIQ: Robust and Efficient Modulation Recognition with Imperfect Spectrum Scans
While critical for the practical progress of spectrum sharing, modulation recognition has so far been investigated under unrealistic assumptions: (i) a transmitter's bandwidth must be scanned alone and in full, (ii) prior knowledge of the technology must be available and (iii) a transmitter must be trustworthy.
Unsupervised Low-light Image Enhancement with Decoupled Networks
In this paper, we tackle the problem of enhancing real-world low-light images with significant noise in an unsupervised fashion.
Example-Guided Image Synthesis across Arbitrary Scenes using Masked Spatial-Channel Attention and Self-Supervision
Example-guided image synthesis has recently been attempted to synthesize an image from a semantic label map and an exemplary image.
Decentralized Multi-player Multi-armed Bandits with No Collision Information
The decentralized stochastic multi-player multi-armed bandit (MP-MAB) problem, where the collision information is not available to the players, is studied in this paper.
Fine-grained Image-to-Image Transformation towards Visual Recognition
In this paper, we aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image, which can thereby benefit the subsequent fine-grained image recognition and few-shot learning tasks.
Example-Guided Scene Image Synthesis using Masked Spatial-Channel Attention and Patch-Based Self-Supervision
Example-guided image synthesis has been recently attempted to synthesize an image from a semantic label map and an exemplary image.
Foreground-aware Image Inpainting
We show that by such disentanglement, the contour completion model predicts reasonable contours of objects, and further substantially improves the performance of image inpainting.
CariGAN: Caricature Generation through Weakly Paired Adversarial Learning
Furthermore, an attention mechanism is introduced to encourage our model to focus on the key facial parts so that more vivid details in these regions can be generated.
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation
In multi-label brain tumor segmentation, class imbalance and inter-class interference are common and challenging problems.
Regional Interactive Image Segmentation Networks
The interactive image segmentation model allows users to iteratively add new inputs for refinement until a satisfactory result is finally obtained.
Ranked #4 on
Interactive Segmentation
on SBD
Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks
The first stage generates videos of realistic contents for each frame.
