Zobrazeno 1 - 5
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pro vyhledávání: '"Garg, Tanmay"'
This paper presents a novel concept learning framework for enhancing model interpretability and performance in visual classification tasks. Our approach appends an unsupervised explanation generator to the primary classifier network and makes use of
Externí odkaz:
http://arxiv.org/abs/2401.04647
Autor:
B, Vimal K, Bachu, Saketh, Garg, Tanmay, Narasimhan, Niveditha Lakshmi, Konuru, Raghavan, Balasubramanian, Vineeth N
Estimating the transferability of publicly available pretrained models to a target task has assumed an important place for transfer learning tasks in recent years. Existing efforts propose metrics that allow a user to choose one model from a pool of
Externí odkaz:
http://arxiv.org/abs/2309.02429
Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detectio
Externí odkaz:
http://arxiv.org/abs/2303.02513
Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors such as the context, geography, socio-political climate, and background of the producers and consumers of the posts play a crucial role in determining if
Externí odkaz:
http://arxiv.org/abs/2202.00126
Autor:
GARG, TANMAY1 tanmay17061@iiitd.ac.in, MASUD, SARAH1 sarahm@iiitd.ac.in, SURESH, THARUN1 tharun20119@iiitd.ac.in, CHAKRABORTY, TANMOY1 tanchak@iiitd.ac.in
Publikováno v:
ACM Computing Surveys. 2023 Suppl13s, Vol. 55, p1-32. 32p.
