Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-supervised Learning with a Teacher-student Network for Generalized
  Attribute Prediction   This paper presents a study on semi-supervised learning to solve the visual
attribute prediction problem. In many applications of vision algorithms, the
precise recognition of visual attributes of objects is important but still
challenging. This is because defining a class hierarchy of attributes is
ambiguous, so training data inevitably suffer from class imbalance and label
sparsity, leading to a lack of effective annotations. An intuitive solution is
to find a method to effectively learn image representations by utilizing
unlabeled images. With that in mind, we propose a multi-teacher-single-student
(MTSS) approach inspired by the multi-task learning and the distillation of
semi-supervised learning. Our MTSS learns task-specific domain experts called
teacher networks using the label embedding technique and learns a unified model
called a student network by forcing a model to mimic the distributions learned
by domain experts. Our experiments demonstrate that our method not only
achieves competitive performance on various benchmarks for fashion attribute
prediction, but also improves robustness and cross-domain adaptability for
unseen domains.

18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio