  Unsupervised anomaly detection encompasses diverse applications in industrial
settings where a high-throughput and precision is imperative. Early works were
centered around one-class-one-model paradigm, which poses significant
challenges in large-scale production environments. Knowledge-distillation based
multi-class anomaly detection promises a low latency with a reasonably good
performance but with a significant drop as compared to one-class version. We
propose a DCAM (Distributed Convolutional Attention Module) which improves the
distillation process between teacher and student networks when there is a high
variance among multiple classes or objects. Integrated multi-scale feature
matching strategy to utilise a mixture of multi-level knowledge from the
feature pyramid of the two networks, intuitively helping in detecting anomalies
of varying sizes which is also an inherent problem in the multi-class scenario.
Briefly, our DCAM module consists of Convolutional Attention blocks distributed
across the feature maps of the student network, which essentially learns to
masks the irrelevant information during student learning alleviating the
"cross-class interference" problem. This process is accompanied by minimizing
the relative entropy using KL-Divergence in Spatial dimension and a
Channel-wise Cosine Similarity between the same feature maps of teacher and
student. The losses enables to achieve scale-invariance and capture non-linear
relationships. We also highlight that the DCAM module would only be used during
training and not during inference as we only need the learned feature maps and
losses for anomaly scoring and hence, gaining a performance gain of 3.92% than
the multi-class baseline with a preserved latency.
