DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets
Data imbalance is a well-known issue in the field of machine learning,
attributable to the cost of data collection, the difficulty of labeling, and
the geographical distribution of the data. In computer vision, bias in data
distribution caused by image appearance remains highly unexplored. Compared to
categorical distributions using class labels, image appearance reveals complex
relationships between objects beyond what class labels provide. Clustering deep
perceptual features extracted from raw pixels gives a richer representation of
the data. This paper presents a novel method for addressing data imbalance in
machine learning. The method computes sample likelihoods based on image
appearance using deep perceptual embeddings and clustering. It then uses these
likelihoods to weigh samples differently during training with a proposed
$\textbf{Generalized Focal Loss}$ function. This loss can be easily integrated
with deep learning algorithms. Experiments validate the method's effectiveness
across autonomous driving vision datasets including KITTI and nuScenes. The
loss function improves state-of-the-art 3D object detection methods, achieving
over $200\%$ AP gains on under-represented classes (Cyclist) in the KITTI
dataset. The results demonstrate the method is generalizable, complements
existing techniques, and is particularly beneficial for smaller datasets and
rare classes. Code is available at:
https://github.com/towardsautonomy/DatasetEquity