395 research outputs found
Reflective inductive inference of recursive functions
AbstractIn this paper, we investigate reflective inductive inference of recursive functions. A reflective IIM is a learning machine that is additionally able to assess its own competence.First, we formalize reflective learning from arbitrary, and from canonical, example sequences. Here, we arrive at four different types of reflection: reflection in the limit, optimistic, pessimistic and exact reflection.Then, we compare the learning power of reflective IIMs with each other as well as with the one of standard IIMs for learning in the limit, for consistent learning of three different types, and for finite learning
Adaptive Context Encoding Module for Semantic Segmentation
The object sizes in images are diverse, therefore, capturing multiple scale
context information is essential for semantic segmentation. Existing context
aggregation methods such as pyramid pooling module (PPM) and atrous spatial
pyramid pooling (ASPP) design different pooling size or atrous rate, such that
multiple scale information is captured. However, the pooling sizes and atrous
rates are chosen manually and empirically. In order to capture object context
information adaptively, in this paper, we propose an adaptive context encoding
(ACE) module based on deformable convolution operation to argument multiple
scale information. Our ACE module can be embedded into other Convolutional
Neural Networks (CNN) easily for context aggregation. The effectiveness of the
proposed module is demonstrated on Pascal-Context and ADE20K datasets. Although
our proposed ACE only consists of three deformable convolution blocks, it
outperforms PPM and ASPP in terms of mean Intersection of Union (mIoU) on both
datasets. All the experiment study confirms that our proposed module is
effective as compared to the state-of-the-art methods
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