4 research outputs found
Deep Fuzzy Tree for Large-Scale Hierarchical Visual Classification
Deep learning models often use a flat softmax layer to classify samples after feature extraction in visual classification tasks. However, it is hard to make a single decision of finding the true label from massive classes. In this scenario, hierarchical classification is proved to be an effective solution and can be utilized to replace the softmax layer. A key issue of hierarchical classification is to construct a good label structure, which is very significant for classification performance. Several works have been proposed to address the issue, but they have some limits and are almost designed heuristically. In this paper, inspired by fuzzy rough set theory, we propose a deep fuzzy tree model which learns a better tree structure and classifiers for hierarchical classification with theory guarantee. Experimental results show the effectiveness and efficiency of the proposed model in various visual classification datasets
Your "Flamingo" is My "Bird": Fine-Grained, or Not
Whether what you see in Figure 1 is a "flamingo" or a "bird", is the question
we ask in this paper. While fine-grained visual classification (FGVC) strives
to arrive at the former, for the majority of us non-experts just "bird" would
probably suffice. The real question is therefore -- how can we tailor for
different fine-grained definitions under divergent levels of expertise. For
that, we re-envisage the traditional setting of FGVC, from single-label
classification, to that of top-down traversal of a pre-defined coarse-to-fine
label hierarchy -- so that our answer becomes
"bird"-->"Phoenicopteriformes"-->"Phoenicopteridae"-->"flamingo". To approach
this new problem, we first conduct a comprehensive human study where we confirm
that most participants prefer multi-granularity labels, regardless whether they
consider themselves experts. We then discover the key intuition that:
coarse-level label prediction exacerbates fine-grained feature learning, yet
fine-level feature betters the learning of coarse-level classifier. This
discovery enables us to design a very simple albeit surprisingly effective
solution to our new problem, where we (i) leverage level-specific
classification heads to disentangle coarse-level features with fine-grained
ones, and (ii) allow finer-grained features to participate in coarser-grained
label predictions, which in turn helps with better disentanglement. Experiments
show that our method achieves superior performance in the new FGVC setting, and
performs better than state-of-the-art on traditional single-label FGVC problem
as well. Thanks to its simplicity, our method can be easily implemented on top
of any existing FGVC frameworks and is parameter-free.Comment: Accepted as an oral of CVPR2021. Code:
https://github.com/PRIS-CV/Fine-Grained-or-No
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System
A key challenge in eXplainable Artificial Intelligence is the well-known
tradeoff between the transparency of an algorithm (i.e., how easily a human can
directly understand the algorithm, as opposed to receiving a post-hoc
explanation), and its accuracy. We report on the design of a new deep network
that achieves improved transparency without sacrificing accuracy. We design a
deep convolutional neuro-fuzzy inference system (DCNFIS) by hybridizing fuzzy
logic and deep learning models and show that DCNFIS performs as accurately as
three existing convolutional neural networks on four well-known datasets. We
furthermore that DCNFIS outperforms state-of-the-art deep fuzzy systems. We
then exploit the transparency of fuzzy logic by deriving explanations, in the
form of saliency maps, from the fuzzy rules encoded in DCNFIS. We investigate
the properties of these explanations in greater depth using the Fashion-MNIST
dataset