  This work presents a novel self-supervised representation learning method to
learn efficient representations without labels on images from a 3DPM sensor
(3-Dimensional Particle Measurement; estimates the particle size distribution
of material) utilizing RGB images and depth maps of mining material on the
conveyor belt. Human annotations for material categories on sensor-generated
data are scarce and cost-intensive. Currently, representation learning without
human annotations remains unexplored for mining materials and does not leverage
on utilization of sensor-generated data. The proposed method, Depth Contrast,
enables self-supervised learning of representations without labels on the 3DPM
dataset by exploiting depth maps and inductive transfer. The proposed method
outperforms material classification over ImageNet transfer learning performance
in fully supervised learning settings and achieves an F1 score of 0.73.
Further, The proposed method yields an F1 score of 0.65 with an 11% improvement
over ImageNet transfer learning performance in a semi-supervised setting when
only 20% of labels are used in fine-tuning. Finally, the Proposed method
showcases improved performance generalization on linear evaluation. The
implementation of proposed method is available on GitHub.
