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Found 15 papers, 10 papers with code
depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers
PyTorch \texttt{2. x} introduces a compiler designed to accelerate deep learning programs.
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.
Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow
In this paper, we propose an end-to-end training method A^2OF for video frame interpolation with event-driven Anisotropic Adjustment of Optical Flows.
Self-organized critical dynamics of RNA virus evolution
We anticipate our approach to be a general formalism to portray RNA virus evolution and help identify potential virus lineages to be concerned.
TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation
Since event camera is a novel sensor, its potential has not been fulfilled due to the lack of processing algorithms.
From Big to Small: Adaptive Learning to Partial-Set Domains
Still, the common requirement of identical class space shared across domains hinders applications of domain adaptation to partial-set domains.
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
(2) The best ranked PTM can either be fine-tuned and deployed if we have no preference for the model's architecture or the target PTM can be tuned by the top $K$ ranked PTMs via a Bayesian procedure that we propose.
Tianshou: a Highly Modularized Deep Reinforcement Learning Library
In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend.
LogME: Practical Assessment of Pre-trained Models for Transfer Learning
In pursuit of a practical assessment method, we propose to estimate the maximum value of label evidence given features extracted by pre-trained models.
Ranked #3 on
Transferability
on classification benchmark
Stochastic Normalization
During training, two branches are stochastically selected to avoid over-depending on some sample statistics, resulting in a strong regularization effect, which we interpret as ``architecture regularization.''
Co-Tuning for Transfer Learning
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.
Ranked #1 on
Transfer Learning
on COCO70
How Does Learning Rate Decay Help Modern Neural Networks?
Despite the popularity of these common beliefs, experiments suggest that they are insufficient in explaining the general effectiveness of lrDecay in training modern neural networks that are deep, wide, and nonconvex.
Universal Domain Adaptation
This paper introduces Universal Domain Adaptation (UDA) that requires no prior knowledge on the label sets.
Ranked #10 on
Universal Domain Adaptation
on DomainNet
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
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 target domain.
Learning to Transfer Examples for Partial Domain Adaptation
Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer.
Ranked #4 on
Partial Domain Adaptation
on ImageNet-Caltech
