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Can we Constrain Concept Bottleneck Models to Learn Semantically
  Meaningful Input Features?

Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?

1 February 2024
Jack Furby
Daniel Cunnington
Dave Braines
Alun D. Preece
ArXivPDFHTML

Papers citing "Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?"

3 / 3 papers shown
Title
Semi-supervised Concept Bottleneck Models
Semi-supervised Concept Bottleneck Models
Lijie Hu
Tianhao Huang
Huanyi Xie
Chenyang Ren
Zhengyu Hu
Lu Yu
Lu Yu
Ping Ma
Di Wang
37
4
0
27 Jun 2024
GlanceNets: Interpretabile, Leak-proof Concept-based Models
GlanceNets: Interpretabile, Leak-proof Concept-based Models
Emanuele Marconato
Andrea Passerini
Stefano Teso
96
64
0
31 May 2022
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
L. V. D. van der Maaten
Kilian Q. Weinberger
PINN
3DV
244
35,884
0
25 Aug 2016
1