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pro vyhledávání: '"Rao, Sukrut"'
Concept Bottleneck Models (CBMs) have recently been proposed to address the 'black-box' problem of deep neural networks, by first mapping images to a human-understandable concept space and then linearly combining concepts for classification. Such mod
Externí odkaz:
http://arxiv.org/abs/2407.14499
Knowledge Distillation (KD) has proven effective for compressing large teacher models into smaller student models. While it is well known that student models can achieve similar accuracies as the teachers, it has also been shown that they nonetheless
Externí odkaz:
http://arxiv.org/abs/2402.03119
Publikováno v:
2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 2023, pp. 1922-1933
Despite being highly performant, deep neural networks might base their decisions on features that spuriously correlate with the provided labels, thus hurting generalization. To mitigate this, 'model guidance' has recently gained popularity, i.e. the
Externí odkaz:
http://arxiv.org/abs/2303.11932
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 6, pp. 4090-4101, June 2024
Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' de
Externí odkaz:
http://arxiv.org/abs/2303.11884
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 10213-10222
Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' de
Externí odkaz:
http://arxiv.org/abs/2205.10435
Publikováno v:
Bartoli, A., Fusiello, A. (eds) Computer Vision - ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science, vol 12539. Springer, Cham
Deep neural networks have been shown to be susceptible to adversarial examples -- small, imperceptible changes constructed to cause mis-classification in otherwise highly accurate image classifiers. As a practical alternative, recent work proposed so
Externí odkaz:
http://arxiv.org/abs/2005.02313
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Incomplete MaxSAT solving aims to quickly find a solution that attempts to minimize the sum of the weights of the unsatisfied soft clauses without providing any optimality guarantees. In this paper, we propose two approximation strategies for improvi
Externí odkaz:
http://arxiv.org/abs/1806.07164
Many real world problems can now be effectively solved using supervised machine learning. A major roadblock is often the lack of an adequate quantity of labeled data for training. A possible solution is to assign the task of labeling data to a crowd,
Externí odkaz:
http://arxiv.org/abs/1803.02781
Deep neural networks are highly performant, but might base their decision on spurious or background features that co-occur with certain classes, which can hurt generalization. To mitigate this issue, the usage of 'model guidance' has gained popularit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5da59412292d856a9093cf840927ebca