Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Venkateswara, Hemanth"'
In the absence of labeled target data, unsupervised domain adaptation approaches seek to align the marginal distributions of the source and target domains in order to train a classifier for the target. Unsupervised domain alignment procedures are cat
Externí odkaz:
http://arxiv.org/abs/2402.06809
The standard closed-set domain adaptation approaches seek to mitigate distribution discrepancies between two domains under the constraint of both sharing identical label sets. However, in realistic scenarios, finding an optimal source domain with ide
Externí odkaz:
http://arxiv.org/abs/2212.01590
Vision transformers require a huge amount of labeled data to outperform convolutional neural networks. However, labeling a huge dataset is a very expensive process. Self-supervised learning techniques alleviate this problem by learning features simil
Externí odkaz:
http://arxiv.org/abs/2210.15722
Publikováno v:
Journal of Computational and Cognitive Engineering. Volume 1, Issue 4, 2022
In contrast to a standard closed-set domain adaptation task, partial domain adaptation setup caters to a realistic scenario by relaxing the identical label set assumption. The fact of source label set subsuming the target label set, however, introduc
Externí odkaz:
http://arxiv.org/abs/2207.08145
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behavior. In this paper we introduc
Externí odkaz:
http://arxiv.org/abs/2201.10711
The generalization power of deep-learning models is dependent on rich-labelled data. This supervision using large-scaled annotated information is restrictive in most real-world scenarios where data collection and their annotation involve huge cost. V
Externí odkaz:
http://arxiv.org/abs/2101.02275
Zero-shot learning (ZSL) addresses the unseen class recognition problem by leveraging semantic information to transfer knowledge from seen classes to unseen classes. Generative models synthesize the unseen visual features and convert ZSL into a class
Externí odkaz:
http://arxiv.org/abs/2007.09549
Autor:
Fakhri, Bijan, McDaniel, Troy, Amor, Heni Ben, Venkateswara, Hemanth, Chowdhury, Abhik, Panchanathan, Sethuraman
As digital worlds become ubiquitous via video games, simulations, virtual and augmented reality, people with disabilities who cannot access those worlds are becoming increasingly disenfranchised. More often than not the design of these environments f
Externí odkaz:
http://arxiv.org/abs/2001.01824
Playlists have become a significant part of our listening experience because of the digital cloud-based services such as Spotify, Pandora, Apple Music. Owing to the meteoric rise in the usage of playlists, recommending playlists is crucial to music s
Externí odkaz:
http://arxiv.org/abs/1907.01098