This paper presents a novel generative model for Computer Aided Design (CAD)
that 1) represents high-level design concepts of a CAD model as a three-level
hierarchical tree of neural codes, from global part arrangement down to local
curve geometry; and 2) controls the generation or completion of CAD models by
specifying the target design using a code tree. Concretely, a novel variant of
a vector quantized VAE with "masked skip connection" extracts design variations
as neural codebooks at three levels. Two-stage cascaded auto-regressive
transformers learn to generate code trees from incomplete CAD models and then
complete CAD models following the intended design. Extensive experiments
demonstrate superior performance on conventional tasks such as random
generation while enabling novel interaction capabilities on conditional
generation tasks. The code is available at
this https URL

本文提出了一种新颖的计算机辅助设计 (CAD) 的生成模型，通过使用神经编码的三级分层树来表示 CAD 模型的高级设计概念，并且指定目标设计，从而控制 CAD 模型的生成或完成。

可控的 CAD 模型生成的 层次化神经编码

Hierarchical Neural Coding for Controllable CAD Model Generation

Incremental learning aims to enable machine learning models to continuously
acquire new knowledge given new classes, while maintaining the knowledge
already learned for old classes. Saving a subset of training samples of
previously seen classes in the memory and replaying them during new training
phases is proven to be an efficient and effective way to fulfil this aim. It is
evident that the larger number of exemplars the model inherits the better
performance it can achieve. However, finding a trade-off between the model
performance and the number of samples to save for each class is still an open
problem for replay-based incremental learning and is increasingly desirable for
real-life applications. In this paper, we approach this open problem by tapping
into a two-step compression approach. The first step is a lossy compression, we
propose to encode input images and save their discrete latent representations
in the form of codes that are learned using a hierarchical Vector Quantised
Variational Autoencoder (VQ-VAE). In the second step, we further compress codes
losslessly by learning a hierarchical latent variable model with bits-back
asymmetric numeral systems (BB-ANS). To compensate for the information lost in
the first step compression, we introduce an Information Back (IB) mechanism
that utilizes real exemplars for a contrastive learning loss to regularize the
training of a classifier. By maintaining all seen exemplars' representations in
the format of `codes', Discrete Representation Replay (DRR) outperforms the
state-of-art method on CIFAR-100 by a margin of 4% accuracy with a much less
memory cost required for saving samples. Incorporated with IB and saving a
small set of old raw exemplars as well, the accuracy of DRR can be further
improved by 2% accuracy.

通过使用分层矢量量化变分自编码器（VQ-VAE）和 bits-back 的不对称数字系统（BB-ANS）进行编码压缩，在对比学习的损失下利用真实实例进行信息回传机制（IB），并将所有已看到的示例表示为‘codes’的格式，从而提高了 CIFAR-100 数据集上 Discrete Representation Replay（DRR）的准确性，实现了递增学习和基于回放的递增学习的信息存储的效率。

IB-DRR: 带信息反馈离散表示重放的增量学习

IB-DRR: Incremental Learning with Information-Back Discrete  Representation Replay

We consider the task of unsupervised extraction of meaningful latent
representations of speech by applying autoencoding neural networks to speech
waveforms. The goal is to learn a representation able to capture high level
semantic content from the signal, e.g.\ phoneme identities, while being
invariant to confounding low level details in the signal such as the underlying
pitch contour or background noise. Since the learned representation is tuned to
contain only phonetic content, we resort to using a high capacity WaveNet
decoder to infer information discarded by the encoder from previous samples.
Moreover, the behavior of autoencoder models depends on the kind of constraint
that is applied to the latent representation. We compare three variants: a
simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder
(VAE), and a discrete Vector Quantized VAE (VQ-VAE). We analyze the quality of
learned representations in terms of speaker independence, the ability to
predict phonetic content, and the ability to accurately reconstruct individual
spectrogram frames. Moreover, for discrete encodings extracted using the
VQ-VAE, we measure the ease of mapping them to phonemes. We introduce a
regularization scheme that forces the representations to focus on the phonetic
content of the utterance and report performance comparable with the top entries
in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.

本研究旨在通过自编码神经网络从语音波形中提取有意义的潜在表示，并比较三个变体：简单维度约束、高斯变分自编码器和离散量化矢量自编码器，成果表明此方法在 ZeroSpeech 2017 任务中获得了可比拟的表现。