Current deep learning classifiers, carry out supervised learning and store class discriminatory information in a set of shared network weights. These weights cannot be easily altered to incrementally learn additional classes, since the classification weights all require retraining to prevent old class information from being lost and also require the previous training data to be present. We present a novel two stage architecture which couples visual feature learning with probabilistic models to represent each class in the form of a Gaussian Mixture Model. By using these independent class representations within our classifier, we outperform a benchmark of an equivalent network with a Softmax head, obtaining increased accuracy for sample sizes smaller than 12 and increased weighted F1 score for 3 imbalanced class profiles in that sample range. When learning new classes our classifier exhibits no catastrophic forgetting issues and only requires the new classes' training images to be present. This enables a database of growing classes over time which can be visually indexed and reasoned over.
翻译:目前深层次的学习分类师,在一组共享网络重量中实施监管的学习和储存阶级歧视性信息。这些重量无法轻易地转换为递增学习更多的班级,因为分类加权数都需要再培训,以防止旧类信息丢失,还需要提供先前的培训数据。我们展示了一个新的两个阶段结构,将视觉学习与概率模型结合起来,以高萨混合模型的形式代表每个班级。通过在分类师内部使用这些独立的阶级代表,我们取得了一个具有软顶部的等同网络的基准,提高了样本大小小于12的准确度,提高了抽样范围内3个不平衡班级的加权F1评分的准确度。在学习新班时,我们的分类师没有灾难性的遗忘问题,只要求新的班级培训图像出现,这样就可以建立一个可以进行视觉索引和推理的不断增长的班级数据库。