  Semi-supervised learning frameworks usually adopt mutual learning approaches
with multiple submodels to learn from different perspectives. To avoid
transferring erroneous pseudo labels between these submodels, a high threshold
is usually used to filter out a large number of low-confidence predictions for
unlabeled data. However, such filtering can not fully exploit unlabeled data
with low prediction confidence. To overcome this problem, in this work, we
propose a mutual learning framework based on pseudo-negative labels. Negative
labels are those that a corresponding data item does not belong. In each
iteration, one submodel generates pseudo-negative labels for each data item,
and the other submodel learns from these labels. The role of the two submodels
exchanges after each iteration until convergence. By reducing the prediction
probability on pseudo-negative labels, the dual model can improve its
prediction ability. We also propose a mechanism to select a few pseudo-negative
labels to feed into submodels. In the experiments, our framework achieves
state-of-the-art results on several main benchmarks. Specifically, with our
framework, the error rates of the 13-layer CNN model are 9.35% and 7.94% for
CIFAR-10 with 1000 and 4000 labels, respectively. In addition, for the
non-augmented MNIST with only 20 labels, the error rate is 0.81% by our
framework, which is much smaller than that of other approaches. Our approach
also demonstrates a significant performance improvement in domain adaptation.
