论文标题
换成脱钩的变分推断，以进行几个射击分类
Transductive Decoupled Variational Inference for Few-Shot Classification
论文作者
论文摘要
从少数样本中学习的多功能性是人类智能的标志。很少有学习能力超越机器的努力。受概率深度学习的承诺和力量的启发，我们提出了一个新颖的变分推理网络，用于几个射击分类（以三叉戟的形式）将图像表示为语义和标记潜在变量，并以交织在一起的方式同时推断它们。为了诱导任务意识，作为三叉戟推理力学的一部分，我们使用一种新型的基于内置的基于注意的转换功能提取模块（我们呼叫attfex）在查询和支持图像上挖掘信息。我们广泛的实验结果证实了三叉戟的功效，并证明，使用最简单的骨干，它在最常见的最常用数据集中的新最先进的是新的最新动物中，以及最多4％和5％的改进，以及最近的挑战性的跨越20％的域名（分别为挑战性的杂货） - > COPES SCEETAIMAIMAIMIMIMIMIMEAGENET（> COPCE） - > COPES SCEETIMAIMIMIMIMIMEAMENET（>）改进）超出了最好的现有跨域基线。可以在我们的GitHub存储库中找到代码和实验：https：//github.com/anujinho/trident
The versatility to learn from a handful of samples is the hallmark of human intelligence. Few-shot learning is an endeavour to transcend this capability down to machines. Inspired by the promise and power of probabilistic deep learning, we propose a novel variational inference network for few-shot classification (coined as TRIDENT) to decouple the representation of an image into semantic and label latent variables, and simultaneously infer them in an intertwined fashion. To induce task-awareness, as part of the inference mechanics of TRIDENT, we exploit information across both query and support images of a few-shot task using a novel built-in attention-based transductive feature extraction module (we call AttFEX). Our extensive experimental results corroborate the efficacy of TRIDENT and demonstrate that, using the simplest of backbones, it sets a new state-of-the-art in the most commonly adopted datasets miniImageNet and tieredImageNet (offering up to 4% and 5% improvements, respectively), as well as for the recent challenging cross-domain miniImagenet --> CUB scenario offering a significant margin (up to 20% improvement) beyond the best existing cross-domain baselines. Code and experimentation can be found in our GitHub repository: https://github.com/anujinho/trident
