  Neuro-symbolic integration aims at harnessing the power of symbolic knowledge
representation combined with the learning capabilities of deep neural networks.
In particular, Logic Tensor Networks (LTNs) allow to incorporate background
knowledge in the form of logical axioms by grounding a first order logic
language as differentiable operations between real tensors. Yet, few studies
have investigated the potential benefits of this approach to improve zero-shot
learning (ZSL) classification. In this study, we present the Fuzzy Logic Visual
Network (FLVN) that formulates the task of learning a visual-semantic embedding
space within a neuro-symbolic LTN framework. FLVN incorporates prior knowledge
in the form of class hierarchies (classes and macro-classes) along with robust
high-level inductive biases. The latter allow, for instance, to handle
exceptions in class-level attributes, and to enforce similarity between images
of the same class, preventing premature overfitting to seen classes and
improving overall performance. FLVN reaches state of the art performance on the
Generalized ZSL (GZSL) benchmarks AWA2 and CUB, improving by 1.3% and 3%,
respectively. Overall, it achieves competitive performance to recent ZSL
methods with less computational overhead. FLVN is available at
https://gitlab.com/grains2/flvn.
