We address the problem of generalized zero-shot semantic segmentation (GZS3)
predicting pixel-wise semantic labels for seen and unseen classes. Most GZS3
methods adopt a generative approach that synthesizes visual features of unseen
classes from corresponding semantic ones (e.g., word2vec) to train novel
classifiers for both seen and unseen classes. Although generative methods show
decent performance, they have two limitations: (1) the visual features are
biased towards seen classes; (2) the classifier should be retrained whenever
novel unseen classes appear. We propose a discriminative approach to address
these limitations in a unified framework. To this end, we leverage visual and
semantic encoders to learn a joint embedding space, where the semantic encoder
transforms semantic features to semantic prototypes that act as centers for
visual features of corresponding classes. Specifically, we introduce
boundary-aware regression (BAR) and semantic consistency (SC) losses to learn
discriminative features. Our approach to exploiting the joint embedding space,
together with BAR and SC terms, alleviates the seen bias problem. At test time,
we avoid the retraining process by exploiting semantic prototypes as a
nearest-neighbor (NN) classifier. To further alleviate the bias problem, we
also propose an inference technique, dubbed Apollonius calibration (AC), that
modulates the decision boundary of the NN classifier to the Apollonius circle
adaptively. Experimental results demonstrate the effectiveness of our
framework, achieving a new state of the art on standard benchmarks.

本文提出了一种基于边界感知回归和语义一致性损失实现零样本语义分割任务的方法，并将其与 Apollonius 校准技术相结合，提高了分类器的泛化性能。实验结果表明该方法在标准数据集上实现了最先进的性能表现。

利用联合嵌入空间进行广义零样本语义分割

Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic  Segmentation

This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving
the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed
framework that extends the Dyna-Q algorithm to integrate planning for
task-completion dialogue policy learning. To obviate DDQ's high dependency on
the quality of simulated experiences, we incorporate an RNN-based discriminator
in D3Q to differentiate simulated experience from real user experience in order
to control the quality of training data. Experiments show that D3Q
significantly outperforms DDQ by controlling the quality of simulated
experience used for planning. The effectiveness and robustness of D3Q is
further demonstrated in a domain extension setting, where the agent's
capability of adapting to a changing environment is tested.

本文提出了一种判别式深度 Dyna-Q (D3Q) 方法来提高 Deep Dyna-Q (DDQ) 框架在任务完成对话策略学习中的有效性和鲁棒性。通过整合基于 RNN 的鉴别器来控制训练数据的质量，实验表明与 DDQ 相比，D3Q 的性能得到了显著提高，并在领域扩展实验中进一步证明了 D3Q 的有效性和鲁棒性。

区分性深层 Dyna-Q: 对话策略学习的强韧规划

Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning

In this paper, we present the Inter-Battery Topic Model (IBTM). Our approach
extends traditional topic models by learning a factorized latent variable
representation. The structured representation leads to a model that marries
benefits traditionally associated with a discriminative approach, such as
feature selection, with those of a generative model, such as principled
regularization and ability to handle missing data. The factorization is
provided by representing data in terms of aligned pairs of observations as
different views. This provides means for selecting a representation that
separately models topics that exist in both views from the topics that are
unique to a single view. This structured consolidation allows for efficient and
robust inference and provides a compact and efficient representation. Learning
is performed in a Bayesian fashion by maximizing a rigorous bound on the
log-likelihood. Firstly, we illustrate the benefits of the model on a synthetic
dataset,. The model is then evaluated in both uni- and multi-modality settings
on two different classification tasks with off-the-shelf convolutional neural
network (CNN) features which generate state-of-the-art results with extremely
compact representations.

本文介绍了 Inter-Battery Topic Model（IBTM）及其基于因式分解的潜变量表示方法，结合了特征选择和正则化等优势，并提供了一种用于单一视角和两个视角数据上的特征抽取与分类的方法。