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A comprehensive survey on test-time adaptation under distribution shifts
Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …
process that can effectively generalize to test samples, even in the presence of distribution …
[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …
domains, partly because of its ability to learn from data and achieve impressive performance …
Contrastive test-time adaptation
Test-time adaptation is a special setting of unsupervised domain adaptation where a trained
model on the source domain has to adapt to the target domain without accessing source …
model on the source domain has to adapt to the target domain without accessing source …
Guiding pseudo-labels with uncertainty estimation for source-free unsupervised domain adaptation
Abstract Standard Unsupervised Domain Adaptation (UDA) methods assume the availability
of both source and target data during the adaptation. In this work, we investigate Source-free …
of both source and target data during the adaptation. In this work, we investigate Source-free …
Deep clustering: A comprehensive survey
Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
Gram: Generative radiance manifolds for 3d-aware image generation
Abstract 3D-aware image generative modeling aims to generate 3D-consistent images with
explicitly controllable camera poses. Recent works have shown promising results by training …
explicitly controllable camera poses. Recent works have shown promising results by training …
How to train your robot with deep reinforcement learning: lessons we have learned
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
acquiring complex behaviors from low-level sensor observations. Although a large portion of …
Cdtrans: Cross-domain transformer for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to a different unlabeled target domain. Most existing UDA methods focus on …
source domain to a different unlabeled target domain. Most existing UDA methods focus on …
Sim-to-real transfer in deep reinforcement learning for robotics: a survey
Deep reinforcement learning has recently seen huge success across multiple areas in the
robotics domain. Owing to the limitations of gathering real-world data, ie, sample inefficiency …
robotics domain. Owing to the limitations of gathering real-world data, ie, sample inefficiency …
Contrastive learning for unpaired image-to-image translation
In image-to-image translation, each patch in the output should reflect the content of the
corresponding patch in the input, independent of domain. We propose a straightforward …
corresponding patch in the input, independent of domain. We propose a straightforward …