1 research outputs found
Metric Learning for Projections Bias of Generalized Zero-shot Learning
Generalized zero-shot learning models (GZSL) aim to recognize samples from
seen or unseen classes using only samples from seen classes as training data.
During inference, GZSL methods are often biased towards seen classes due to the
visibility of seen class samples during training. Most current GZSL methods try
to learn an accurate projection function (from visual space to semantic space)
to avoid bias and ensure the effectiveness of GZSL methods. However, during
inference, the computation of distance will be important when we classify the
projection of any sample into its nearest class since we may learn a biased
projection function in the model. In our work, we attempt to learn a
parameterized Mahalanobis distance within the framework of VAEGAN (Variational
Autoencoder \& Generative Adversarial Networks), where the weight matrix
depends on the network's output. In particular, we improved the network
structure of VAEGAN to leverage the discriminative models of two branches to
separately predict the seen samples and the unseen samples generated by this
seen one. We proposed a new loss function with two branches to help us learn
the optimized Mahalanobis distance representation. Comprehensive evaluation
benchmarks on four datasets demonstrate the superiority of our method over the
state-of-the-art counterparts. Our codes are available at
https://anonymous.4open.science/r/111hxr.Comment: 9 pages, 2 figure