  Event cameras are paradigm-shifting novel sensors that report asynchronous,
per-pixel brightness changes called 'events' with unparalleled low latency.
This makes them ideal for high speed, high dynamic range scenes where
conventional cameras would fail. Recent work has demonstrated impressive
results using Convolutional Neural Networks (CNNs) for video reconstruction and
optic flow with events. We present strategies for improving training data for
event based CNNs that result in 20-40% boost in performance of existing
state-of-the-art (SOTA) video reconstruction networks retrained with our
method, and up to 15% for optic flow networks. A challenge in evaluating event
based video reconstruction is lack of quality ground truth images in existing
datasets. To address this, we present a new High Quality Frames (HQF) dataset,
containing events and ground truth frames from a DAVIS240C that are
well-exposed and minimally motion-blurred. We evaluate our method on HQF +
several existing major event camera datasets.
