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IS THE LABEL TRUSTFUL: TRAINING BETTER DEEP LEARNING MODEL VIA UNCERTAINTY MINING NET
During the training stage, we utilize all the available data (labeled and unlabeled) to train the classifier via a semi-supervised generative framework.
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression
State-of-the-art deep model compression methods exploit the low-rank approximation and sparsity pruning to remove redundant parameters from a learned hidden layer.
CATS: A Color and Thermal Stereo Benchmark
We present the Color And Thermal Stereo (CATS) benchmark, a dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth (< 2mm) generated from a LiDAR.
Robust Shape Registration using Fuzzy Correspondences
We present a shape registration approach that solves for the transformation using fuzzy correspondences to maximize the overlap between the given shape and the target shape.
Material Classification With Thermal Imagery
Typical algorithms use color and texture information for classification, but there are problems due to varying lighting conditions and diversity of colors in a single material class.
