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Found 11 papers, 1 papers with code
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
A dense block was introduced to improve the performance of extractor in DRU.
Ranked #2 on
JPEG Artifact Correction
on LIVE1 (Quality 20 Color)
Color Image Compression Artifact Reduction
Image Compression Artifact Reduction
+3
S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction
Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction.
Ranked #4 on
JPEG Artifact Correction
on LIVE1 (Quality 20 Color)
JPEG Artifact Correction
Jpeg Compression Artifact Reduction
AIM 2019 Challenge on Image Demoireing: Methods and Results
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
DGL-GAN: Discriminator Guided Learning for GAN Compression
Moreover, DGL-GAN is also effective in boosting the performance of original uncompressed GANs.
MPC: Multi-View Probabilistic Clustering
Despite the promising progress having been made, the two challenges of multi-view clustering (MVC) are still waiting for better solutions: i) Most existing methods are either not qualified or require additional steps for incomplete multi-view clustering and ii) noise or outliers might significantly degrade the overall clustering performance.
Dynamic Supervisor for Cross-dataset Object Detection
The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learning into semi-supervised learning.
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection
The likelihood maps generated by the SLV module are used to supervise the feature learning of the backbone network, encouraging the network to attend to wider and more diverse areas of the image.
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method.
Boosting Out-of-distribution Detection with Typical Features
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios.
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective
Additionally, our experiments demonstrate that model selection is non-trivial for OOD detection and should be considered as an integral of the proposed method, which differs from the claim in existing works that proposed methods are universal across different models.
Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems
We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection.
