Inductive biases are crucial in disentangled representation learning for
narrowing down an underspecified solution set. In this work, we consider
endowing a neural network autoencoder with three select inductive biases from
the literature: data compression into a grid-like latent space via
quantization, collective independence amongst latents, and minimal functional
influence of any latent on how other latents determine data generation. In
principle, these inductive biases are deeply complementary: they most directly
specify properties of the latent space, encoder, and decoder, respectively. In
practice, however, naively combining existing techniques instantiating these
inductive biases fails to yield significant benefits. To address this, we
propose adaptations to the three techniques that simplify the learning problem,
equip key regularization terms with stabilizing invariances, and quash
degenerate incentives. The resulting model, Tripod, achieves state-of-the-art
results on a suite of four image disentanglement benchmarks. We also verify
that Tripod significantly improves upon its naive incarnation and that all
three of its "legs" are necessary for best performance.

在这项研究中，我们通过将神经网络自编码器与三种从文献中选取的归纳偏差相结合，即通过量化将数据压缩成网格状潜在空间、潜在变量之间的集体独立性和最小功能对其他潜在变量数据生成的功能影响减至最小，提出了一种新的模型 Tripod，该模型在四个图像分离基准测试中取得了最先进的结果，实验证明 Tripod 明显优于其朴素形式，并且其三个 “腿” 对于最佳性能是必要的。

三脚架：用于解缠合表示学习的三个互补归纳偏差

Tripod: Three Complementary Inductive Biases for Disentangled  Representation Learning

Few-shot image generation and few-shot image translation are two related
tasks, both of which aim to generate new images for an unseen category with
only a few images. In this work, we make the first attempt to adapt few-shot
image translation method to few-shot image generation task. Few-shot image
translation disentangles an image into style vector and content map. An unseen
style vector can be combined with different seen content maps to produce
different images. However, it needs to store seen images to provide content
maps and the unseen style vector may be incompatible with seen content maps. To
adapt it to few-shot image generation task, we learn a compact dictionary of
local content vectors via quantizing continuous content maps into discrete
content maps instead of storing seen images. Furthermore, we model the
autoregressive distribution of discrete content map conditioned on style
vector, which can alleviate the incompatibility between content map and style
vector. Qualitative and quantitative results on three real datasets demonstrate
that our model can produce images of higher diversity and fidelity for unseen
categories than previous methods.

本研究将少样本图像转换方法应用于少样本图像生成任务中，通过量化连续内容地图为离散内容地图，学习本地内容向量的简洁字典，利用离散内容地图的自回归分布模型缓解内容地图和风格向量的不兼容性。实验结果表明，相对于以前的方法，该模型能够为未见类别生成更多样化和更真实的图像。

利用离散内容表示进行少样本图像生成

Few-shot Image Generation Using Discrete Content Representation

While representation learning aims to derive interpretable features for
describing visual data, representation disentanglement further results in such
features so that particular image attributes can be identified and manipulated.
However, one cannot easily address this task without observing ground truth
annotation for the training data. To address this problem, we propose a novel
deep learning model of Cross-Domain Representation Disentangler (CDRD). By
observing fully annotated source-domain data and unlabeled target-domain data
of interest, our model bridges the information across data domains and
transfers the attribute information accordingly. Thus, cross-domain joint
feature disentanglement and adaptation can be jointly performed. In the
experiments, we provide qualitative results to verify our disentanglement
capability. Moreover, we further confirm that our model can be applied for
solving classification tasks of unsupervised domain adaptation, and performs
favorably against state-of-the-art image disentanglement and translation
methods.

本研究提出了一种用于跨域图像特征提取和 unsupervised domain adaptation 的深度学习模型，实现了特征的 disentanglement 和适应，并在实验中证实了其有效性和优越性。