Search Results for author:
Found 12 papers, 4 papers with code
Semantic Is Enough: Only Semantic Information For NeRF Reconstruction
This research aims to extend the Semantic Neural Radiance Fields (Semantic-NeRF) model by focusing solely on semantic output and removing the RGB output component.
VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges.
Efficient ConvBN Blocks for Transfer Learning and Beyond
Subsequently, we propose a novel Tune mode to bridge the gap between Eval mode and Deploy mode.
Railway Network Delay Evolution: A Heterogeneous Graph Neural Network Approach
This paper aims to develop a heterogeneous graph neural network (HetGNN) model, which can address different types of nodes (i. e., heterogeneous nodes), to investigate the train delay evolution on railway networks.
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection
In this paper, we propose a Robust GAN-inversion (RGI) method with a provable robustness guarantee to achieve image restoration under unknown \textit{gross} corruptions, where a small fraction of pixels are completely corrupted.
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection
In this study, we propose an improved model called DeSTSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework.
Ranked #35 on
Anomaly Detection
on MVTec AD
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation
To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation.
Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey
Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process.
Information Gain Propagation: a new way to Graph Active Learning with Soft Labels
Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort.
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation
On active learning task, our method achieves 97. 0% Top-1 Accuracy on CIFAR10 with 0. 1% annotated data, and 83. 9% Top-1 Accuracy on CIFAR100 with 10% annotated data.
RIM: Reliable Influence-based Active Learning on Graphs
Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph.
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer
While single-shot quantized neural architecture search enjoys flexibility in both model architecture and quantization policy, the combined search space comes with many challenges, including instability when training the weight-sharing supernet and difficulty in navigating the exponentially growing search space.
Hardware Aware Neural Architecture Search
Model Optimization
+2
