Search Results for author:
Found 8 papers, 1 papers with code
SEFD: Learning to Distill Complex Pose and Occlusion
This paper addresses the problem of three-dimensional (3D) human mesh estimation in complex poses and occluded situations.
Ranked #1 on
2D Human Pose Estimation
on OCHuman
AnoSeg: Anomaly Segmentation Network Using Self-Supervised Learning
Finally, the coordinate channel, which represents the pixel location information, is concatenated to an input of AnoSeg to consider the positional relationship of each pixel in the image.
Ranked #57 on
Anomaly Detection
on MVTec AD
Mitigating Memorization in Sample Selection for Learning with Noisy Labels
Because deep learning is vulnerable to noisy labels, sample selection techniques, which train networks with only clean labeled data, have attracted a great attention.
Core-set Sampling for Efficient Neural Architecture Search
Neural architecture search (NAS), an important branch of automatic machine learning, has become an effective approach to automate the design of deep learning models.
Selective Focusing Learning in Conditional GANs
Conditional generative adversarial networks (cGANs) have demonstrated remarkable success due to their class-wise controllability and superior quality for complex generation tasks.
Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation
By applying the attention map to an image feature map, ADGAN learns the normal class distribution from which the useless region is removed, and it is possible to greatly reduce the problem difficulty of the anomaly detection task.
Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Progressive Step Learning
The bidirectional boundary region rearrangement enables the generation of the missing region using bidirectional information similar to that of the image inpainting task, thereby generating the higher quality than the conventional methods using unidirectional information.
Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback
In the proposed system, channel estimation and quantization for limited feedback are handled by a receiver deep neural network (DNN).
