  Detection of out-of-distribution samples is one of the critical tasks for
real-world applications of computer vision. The advancement of deep learning
has enabled us to analyze real-world data which contain unexplained samples,
accentuating the need to detect out-of-distribution instances more than before.
GAN-based approaches have been widely used to address this problem due to their
ability to perform distribution fitting; however, they are accompanied by
training instability and mode collapse. We propose a simple yet efficient
reconstruction-based method that avoids adding complexities to compensate for
the limitations of GAN models while outperforming them. Unlike previous
reconstruction-based works that only utilize reconstruction error or generated
samples, our proposed method simultaneously incorporates both of them in the
detection task. Our model, which we call "Connective Novelty Detection" has two
subnetworks, an autoencoder, and a binary classifier. The autoencoder learns
the representation of the positive class by reconstructing them. Then, the
model creates negative and connected positive examples using real and generated
samples. Negative instances are generated via manipulating the real data, so
their distribution is close to the positive class to achieve a more accurate
boundary for the classifier. To boost the robustness of the detection to
reconstruction error, connected positive samples are created by combining the
real and generated samples. Finally, the binary classifier is trained using
connected positive and negative examples. We demonstrate a considerable
improvement in novelty detection over state-of-the-art methods on MNIST and
Caltech-256 datasets.
