Abstract Domain adaptation aims to transfer knowledge in the presence of the domain gap.
Existing domain adaptation methods rely on rich prior knowledge about the relationship …

Abstract Domain adaptation is critical for learning in new and unseen environments. With
domain adversarial training, deep networks can learn disentangled and transferable features …

This paper studies task adaptive pre-trained model selection, an underexplored problem of
assessing pre-trained models for the target task and select best ones from the model zoo\…

Learning rate decay (lrDecay) is a \emph{de facto} technique for training modern neural
networks. It starts with a large learning rate and then decays it multiple times. It is empirically …

In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement
learning (DRL) that uses PyTorch as its backend. Tianshou intends to be research-…

Fine-tuning pre-trained deep neural networks (DNNs) to a target dataset, also known as
transfer learning, is widely used in computer vision and NLP. Because task-specific layers …

Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich
labeled data in a source domain to boost the performance on related but unlabeled data in a …

Fine-tuning pre-trained deep networks on a small dataset is an important component in the
deep learning pipeline. A critical problem in fine-tuning is how to avoid over-fitting when data …

In the past years, attention-based Transformers have swept across the field of computer
vision, starting a new stage of backbones in semantic segmentation. Nevertheless, semantic …

Recording fast motion in a high FPS (frame-per-second) requires expensive high-speed
cameras. As an alternative, interpolating low-FPS videos from commodity cameras has attracted …