Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

Neural architecture search (NAS) has shown great promise in designing state-of-the-art
(SOTA) models that are both accurate and efficient. Recently, two-stage NAS, eg BigNAS …

Unsupervised learning has recently made exceptional progress because of the
development of more effective contrastive learning methods. However, CNNs are prone to …

This work addresses the unsupervised adaptation of an existing object detector to a new
target domain. We assume that a large number of unlabeled videos from this domain are …

Recent empirical studies show that adversarial topic models (ATM) can successfully capture
semantic patterns of the document by differentiating a document with another dissimilar …

We present a task-specific approach to synthetic data generation. Our framework employs a
trainable synthesizer network that is optimized to produce meaningful training samples by …

Large-scale datasets with high-quality labels are desired for training accurate deep learning
models. However, due to the annotation cost, datasets in medical imaging are often either …

In semi-supervised learning, unlabeled samples can be utilized through augmentation and
consistency regularization. However, we observed certain samples, even undergoing strong …

Accurate and efficient orchard tree inventories are essential for acquiring up-to-date
information, which is necessary for effective treatments and crop insurance purposes …