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FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
YongganFu, HaoranYou, YangZhao....
Published date-12/01/2020
Quantization
Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due …
Field-wise Learning for Multi-field Categorical Data
ZhibinLi, JianZhang, YongshunGong....
Published date-12/01/2020
We propose a new method for learning with multi-field categorical data. Multi-field categorical data are usually collected over many heterogeneous groups. These groups can reflect in the categories under a …
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
ZixuanXu, BanghuaiLi, YeYuan....
Published date-12/01/2020
PedestrianDetection
Recently significant progress has been made in pedestrian detection, but it remains challenging to achieve high performance in occluded and crowded scenes. It could be mostly attributed to the widely …
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
AnkitGoyal, KaiyuYang, DaweiYang....
Published date-12/01/2020
Understanding spatial relations (e.g., laptop on table) in visual input is important for both humans and robots. Existing datasets are insufficient as they lack large-scale, high-quality 3D ground truth information, …
Analysis of Drifting Features
FabianHinder, JonathanJakob, BarbaraHammer....
Published date-12/01/2020
FeatureSelection
The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. We are interested in an identification of those features, …
HRN: A Holistic Approach to One Class Learning
WenpengHu, MengyuWang, QiQin....
Published date-12/01/2020
AnomalyDetection, ImageClassification
Existing neural network based one-class learning methods mainly use various forms of auto-encoders or GAN style adversarial training to learn a latent representation of the given one class of data. …