  In astronomical surveys, such as the Zwicky Transient Facility, supernovae
(SNe) are relatively uncommon objects compared to other classes of variable
events. Along with this scarcity, the processing of multi-band light-curves is
a challenging task due to the highly irregular cadence, long time gaps,
missing-values, few observations, etc. These issues are particularly
detrimental to the analysis of transient events: SN-like light-curves. We offer
three main contributions: 1) Based on temporal modulation and attention
mechanisms, we propose a Deep attention model (TimeModAttn) to classify
multi-band light-curves of different SN types, avoiding photometric or
hand-crafted feature computations, missing-value assumptions, and explicit
imputation/interpolation methods. 2) We propose a model for the synthetic
generation of SN multi-band light-curves based on the Supernova Parametric
Model, allowing us to increase the number of samples and the diversity of
cadence. Thus, the TimeModAttn model is first pre-trained using synthetic
light-curves. Then, a fine-tuning process is performed. The TimeModAttn model
outperformed other Deep Learning models, based on Recurrent Neural Networks, in
two scenarios: late-classification and early-classification. Also, the
TimeModAttn model outperformed a Balanced Random Forest (BRF) classifier
(trained with real data), increasing the balanced-$F_1$score from $\approx.525$
to $\approx.596$. When training the BRF with synthetic data, this model
achieved similar performance to the TimeModAttn model proposed while still
maintaining extra advantages. 3) We conducted interpretability experiments.
High attention scores were obtained for observations earlier than and close to
the SN brightness peaks. This also correlated with an early highly variability
of the learned temporal modulation.
