Applications of generative adversarial networks (gans): An updated review
Generative adversarial networks (GANs) present a way to learn deep representations
without extensively annotated training data. These networks achieve learning through …
without extensively annotated training data. These networks achieve learning through …
Unsupervised person re-identification via multi-label classification
The challenge of unsupervised person re-identification (ReID) lies in learning discriminative
features without true labels. This paper formulates unsupervised person ReID as a multi …
features without true labels. This paper formulates unsupervised person ReID as a multi …
On mutual information maximization for representation learning
M Tschannen, J Djolonga, PK Rubenstein… - ar**_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.pdf" data-clk="hl=ja&sa=T&oi=gga&ct=gga&cd=7&d=9763356590785048774&ei=Yn-sZ7C_KY_B6rQPrNOHuAc" data-clk-atid="xjA2QRdpfocJ" target="_blank">[PDF] thecvf.com
Self-similarity grou**: A simple unsupervised cross domain adaptation approach for person re-identification
Abstract Domain adaptation in person re-identification (re-ID) has always been a
challenging task. In this work, we explore how to harness the similar natural characteristics …
challenging task. In this work, we explore how to harness the similar natural characteristics …
Unsupervised person re-identification via softened similarity learning
Person re-identification (re-ID) is an important topic in computer vision. This paper studies
the unsupervised setting of re-ID, which does not require any labeled information and thus is …
the unsupervised setting of re-ID, which does not require any labeled information and thus is …
Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification
Person re-identification (re-ID) models trained on one domain often fail to generalize well to
another. In our attempt, we present a``learning via translation''framework. In the baseline, we …
another. In our attempt, we present a``learning via translation''framework. In the baseline, we …