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 …
A review of single-source deep unsupervised visual domain adaptation
Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …
Test-time classifier adjustment module for model-agnostic domain generalization
This paper presents a new algorithm for domain generalization (DG),\textit {test-time
template adjuster (T3A)}, aiming to robustify a model to unknown distribution shift. Unlike …
template adjuster (T3A)}, aiming to robustify a model to unknown distribution shift. Unlike …
Swad: Domain generalization by seeking flat minima
Abstract Domain generalization (DG) methods aim to achieve generalizability to an unseen
target domain by using only training data from the source domains. Although a variety of DG …
target domain by using only training data from the source domains. Although a variety of DG …
Selfreg: Self-supervised contrastive regularization for domain generalization
In general, an experimental environment for deep learning assumes that the training and the
test dataset are sampled from the same distribution. However, in real-world situations, a …
test dataset are sampled from the same distribution. However, in real-world situations, a …
Improving out-of-distribution robustness via selective augmentation
Abstract Machine learning algorithms typically assume that training and test examples are
drawn from the same distribution. However, distribution shift is a common problem in real …
drawn from the same distribution. However, distribution shift is a common problem in real …
Sharpness-aware gradient matching for domain generalization
The goal of domain generalization (DG) is to enhance the generalization capability of the
model learned from a source domain to other unseen domains. The recently developed …
model learned from a source domain to other unseen domains. The recently developed …
Pcl: Proxy-based contrastive learning for domain generalization
Abstract Domain generalization refers to the problem of training a model from a collection of
different source domains that can directly generalize to the unseen target domains. A …
different source domains that can directly generalize to the unseen target domains. A …
A fine-grained analysis on distribution shift
Robustness to distribution shifts is critical for deploying machine learning models in the real
world. Despite this necessity, there has been little work in defining the underlying …
world. Despite this necessity, there has been little work in defining the underlying …
Svformer: Semi-supervised video transformer for action recognition
Semi-supervised action recognition is a challenging but critical task due to the high cost of
video annotations. Existing approaches mainly use convolutional neural networks, yet …
video annotations. Existing approaches mainly use convolutional neural networks, yet …