7 citations · 13 across the 4 of their papers we have counts for
4 papers
PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck
Thang M. Pham, Peijie Chen, Tin Nguyen +3
CLIP-based classifiers rely on the prompt containing a {class name} that is known to the text encoder. Therefore, they perform poorly on new classes or the classes whose names rare…
How explainable are adversarially-robust CNNs?
Mehdi Nourelahi, Lars Kotthoff, Peijie Chen +1
Three important criteria of existing convolutional neural networks (CNNs) are (1) test-set accuracy; (2) out-of-distribution accuracy; and (3) explainability. While these criteria…
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs
Peijie Chen, Chirag Agarwal, Anh Nguyen
Increasingly more similarities between human vision and convolutional neural networks (CNNs) have been revealed in the past few years. Yet, vanilla CNNs often fall short in general…
Neurons Activation Visualization and Information Theoretic Analysis
Longwei Wang, Peijie Chen
Understanding the inner working mechanism of deep neural networks (DNNs) is essential and important for researchers to design and improve the performance of DNNs. In this work, the…