  The paper proposes a semantic clustering based deduction learning by
mimicking the learning and thinking process of human brains. Human beings can
make judgments based on experience and cognition, and as a result, no one would
recognize an unknown animal as a car. Inspired by this observation, we propose
to train deep learning models using the clustering prior that can guide the
models to learn with the ability of semantic deducing and summarizing from
classification attributes, such as a cat belonging to animals while a car
pertaining to vehicles. %Specifically, if an image is labeled as a cat, then
the model is trained to learn that "this image is totally not any random class
that is the outlier of animal". The proposed approach realizes the high-level
clustering in the semantic space, enabling the model to deduce the relations
among various classes during the learning process. In addition, the paper
introduces a semantic prior based random search for the opposite labels to
ensure the smooth distribution of the clustering and the robustness of the
classifiers. The proposed approach is supported theoretically and empirically
through extensive experiments. We compare the performance across
state-of-the-art classifiers on popular benchmarks, and the generalization
ability is verified by adding noisy labeling to the datasets. Experimental
results demonstrate the superiority of the proposed approach.
