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pro vyhledávání: '"Jang, Huiwon"'
Efficient tokenization of videos remains a challenge in training vision models that can process long videos. One promising direction is to develop a tokenizer that can encode long video clips, as it would enable the tokenizer to leverage the temporal
Externí odkaz:
http://arxiv.org/abs/2411.14762
Autor:
Yu, Sihyun, Kwak, Sangkyung, Jang, Huiwon, Jeong, Jongheon, Huang, Jonathan, Shin, Jinwoo, Xie, Saining
Recent studies have shown that the denoising process in (generative) diffusion models can induce meaningful (discriminative) representations inside the model, though the quality of these representations still lags behind those learned through recent
Externí odkaz:
http://arxiv.org/abs/2410.06940
Adversarial robustness has been conventionally believed as a challenging property to encode for neural networks, requiring plenty of training data. In the recent paradigm of adopting off-the-shelf models, however, access to their training data is oft
Externí odkaz:
http://arxiv.org/abs/2407.18658
Self-supervised learning of image representations by predicting future frames is a promising direction but still remains a challenge. This is because of the under-determined nature of frame prediction; multiple potential futures can arise from a sing
Externí odkaz:
http://arxiv.org/abs/2406.07398
Despite its practical importance across a wide range of modalities, recent advances in self-supervised learning (SSL) have been primarily focused on a few well-curated domains, e.g., vision and language, often relying on their domain-specific knowled
Externí odkaz:
http://arxiv.org/abs/2310.16318
Unsupervised meta-learning aims to learn generalizable knowledge across a distribution of tasks constructed from unlabeled data. Here, the main challenge is how to construct diverse tasks for meta-learning without label information; recent works have
Externí odkaz:
http://arxiv.org/abs/2303.00996
Unsupervised anomaly detection is coming into the spotlight these days in various practical domains due to the limited amount of anomaly data. One of the major approaches for it is a normalizing flow which pursues the invertible transformation of a c
Externí odkaz:
http://arxiv.org/abs/2210.14913
Akademický článek
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Autor:
Chang, Chung-Kai, Yu, Hyun Jung, Jang, Huiwon, Hung, Ting-Hsiang, Kim, Jihan, Lee, Jong Suk, Kang, Dun-Yen
Publikováno v:
In Journal of Membrane Science Letters 5 December 2021 1(1)
Akademický článek
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