Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift. The goal is to categorize unlabeled target samples, either into one of the "known" categories or into a single "unknown" category. A major problem in UniDA is negative transfer, i.e. misalignment of "known" and "unknown" classes. To this end, we first uncover an intriguing tradeoff between negative-transfer-risk and domain-invariance exhibited at different layers of a deep network. It turns out we can strike a balance between these two metrics at a mid-level layer. Towards designing an effective framework based on this insight, we draw motivation from Bag-of-visual-Words (BoW). Word-prototypes in a BoW-like representation of a mid-level layer would represent lower-level visual primitives that are likely to be unaffected by the category-shift in the high-level features. We develop modifications that encourage learning of word-prototypes followed by word-histogram based classification. Following this, subsidiary prototype-space alignment (SPA) can be seen as a closed-set alignment problem, thereby avoiding negative transfer. We realize this with a novel word-histogram-related pretext task to enable closed-set SPA, operating in conjunction with goal task UniDA. We demonstrate the efficacy of our approach on top of existing UniDA techniques, yielding state-of-the-art performance across three standard UniDA and Open-Set DA object recognition benchmarks.
翻译:通用域适应( UniDA) 处理域档和类档两个数据集之间的知识转移问题。 目标是将未贴标签的目标样本分为“ 已知” 类别或单一“ 未知” 类别。 UniDA 的主要问题是负转移, 即“ 已知” 和“ 未知” 类的错配。 为此, 我们首先发现在深层次网络的不同层次上, 负转移风险和域错位之间的偏差。 结果是我们可以在中等层次上找到这两个指标之间的平衡。 为了根据这个洞察来设计一个有效的框架, 我们从Bag- 视觉- Words (BoW) 中的一个主要问题是负转移, 即“ 已知” 和“ 未知” 类的偏差。 我们首先发现低层次的视觉原始, 可能不受高层次上层网络的分类的影响。 我们开发了一些修改, 鼓励学习以文字缩略图为基础的对象类型在中间层层层层中找到一个有效的框架框架。 我们可以看到, 以这个与新版本的轨道的升级的升级任务, 我们可以看到一个与新版本的升级的升级任务。