Emotion Recognition

A Transformer is a model architecture that eschews recurrence and instead relies entirely on an attention mechanism to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of attention mechanisms allows for significantly more parallelization than methods like RNNs and CNNs.

Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension $d\_{model}$ as the embeddings, so that the two can be summed. In the original implementation, sine and cosine functions of different frequencies are used: $$ \text{PE}\left(pos, 2i\right) = \sin\left(pos/10000^{2i/d\_{model}}\right) $$ $$ \text{PE}\left(pos, 2i+1\right) = \cos\left(pos/10000^{2i/d\_{model}}\right) $$ where $pos$ is the position and $i$ is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from $2\pi$ to $10000 \dot 2\pi$. This function was chosen because the authors hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $\text{PE}\_{pos+k}$ can be represented as a linear function of $\text{PE}\_{pos}$. Image Source: D2L.ai

BERT, or Bidirectional Encoder Representations from Transformers, improves upon standard Transformers by removing the unidirectionality constraint by using a masked language model (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a next sentence prediction task that jointly pre-trains text-pair representations. There are two steps in BERT: pre-training and fine-tuning. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they are initialized with the same pre-trained parameters.

Diffusion models applied to latent spaces, which are normally built with (Variational) Autoencoders.

DDPG, or Deep Deterministic Policy Gradient, is an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. It combines the actor-critic approach with insights from DQNs: in particular, the insights that 1) the network is trained off-policy with samples from a replay buffer to minimize correlations between samples, and 2) the network is trained with a target Q network to give consistent targets during temporal difference backups. DDPG makes use of the same ideas along with batch normalization.

The Two Time-scale Update Rule (TTUR) is an update rule for generative adversarial networks trained with stochastic gradient descent. TTUR has an individual learning rate for both the discriminator and the generator. The main premise is that the discriminator converges to a local minimum when the generator is fixed. If the generator changes slowly enough, then the discriminator still converges, since the generator perturbations are small. Besides ensuring convergence, the performance may also improve since the discriminator must first learn new patterns before they are transferred to the generator. In contrast, a generator which is overly fast, drives the discriminator steadily into new regions without capturing its gathered information.

Off-Diagonal Orthogonal Regularization is a modified form of orthogonal regularization originally used in BigGAN. The original orthogonal regularization is known to be limiting so the authors explore several variants designed to relax the constraint while still imparting the desired smoothness to the models. They opt for a modification where they remove diagonal terms from the regularization, and aim to minimize the pairwise cosine similarity between filters but does not constrain their norm: $$ R\_{\beta}\left(W\right) = \beta|| W^{T}W \odot \left(\mathbf{1}-I\right) ||^{2}\_{F} $$ where $\mathbf{1}$ denotes a matrix with all elements set to 1. The authors sweep $\beta$ values and select $10^{−4}$.

BigGAN is a type of generative adversarial network that was designed for scaling generation to high-resolution, high-fidelity images. It includes a number of incremental changes and innovations. The baseline and incremental changes are: - Using SAGAN as a baseline with spectral norm. for G and D, and using TTUR. - Using a Hinge Loss GAN objective - Using class-conditional batch normalization to provide class information to G (but with linear projection not MLP. - Using a projection discriminator for D to provide class information to D. - Evaluating with EWMA of G's weights, similar to ProGANs. The innovations are: - Increasing batch sizes, which has a big effect on the Inception Score of the model. - Increasing the width in each layer leads to a further Inception Score improvement. - Adding skip connections from the latent variable $z$ to further layers helps performance. - A new variant of Orthogonal Regularization.

HRNet, or High-Resolution Net, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a high-resolution convolution stream, gradually add high-to-low resolution convolution streams one by one, and connect the multi-resolution streams in parallel. The resulting network consists of several ($4$ in the paper) stages and the $n$th stage contains $n$ streams corresponding to $n$ resolutions. The authors conduct repeated multi-resolution fusions by exchanging the information across the parallel streams over and over.

Cascade R-CNN is an object detection architecture that seeks to address problems with degrading performance with increased IoU thresholds (due to overfitting during training and inference-time mismatch between IoUs for which detector is optimal and the inputs). It is a multi-stage extension of the R-CNN, where detector stages deeper into the cascade are sequentially more selective against close false positives. The cascade of R-CNN stages are trained sequentially, using the output of one stage to train the next. This is motivated by the observation that the output IoU of a regressor is almost invariably better than the input IoU. Cascade R-CNN does not aim to mine hard negatives. Instead, by adjusting bounding boxes, each stage aims to find a good set of close false positives for training the next stage. When operating in this manner, a sequence of detectors adapted to increasingly higher IoUs can beat the overfitting problem, and thus be effectively trained. At inference, the same cascade procedure is applied. The progressively improved hypotheses are better matched to the increasing detector quality at each stage.

Cascade Mask R-CNN extends Cascade R-CNN to instance segmentation, by adding a mask head to the cascade. In the Mask R-CNN, the segmentation branch is inserted in parallel to the detection branch. However, the Cascade R-CNN has multiple detection branches. This raises the questions of 1) where to add the segmentation branch and 2) how many segmentation branches to add. The authors consider three strategies for mask prediction in the Cascade R-CNN. The first two strategies address the first question, adding a single mask prediction head at either the first or last stage of the Cascade R-CNN. Since the instances used to train the segmentation branch are the positives of the detection branch, their number varies in these two strategies. Placing the segmentation head later on the cascade leads to more examples. However, because segmentation is a pixel-wise operation, a large number of highly overlapping instances is not necessarily as helpful as for object detection, which is a patch-based operation. The third strategy addresses the second question, adding a segmentation branch to each cascade stage. This maximizes the diversity of samples used to learn the mask prediction task. At inference time, all three strategies predict the segmentation masks on the patches produced by the final object detection stage, irrespective of the cascade stage on which the segmentation mask is implemented and how many segmentation branches there are.

BigGAN-deep is a deeper version (4x) of BigGAN. The main difference is a slightly differently designed residual block. Here the $z$ vector is concatenated with the conditional vector without splitting it into chunks. It is also based on residual blocks with bottlenecks. BigGAN-deep uses a different strategy than BigGAN aimed at preserving identity throughout the skip connections. In G, where the number of channels needs to be reduced, BigGAN-deep simply retains the first group of channels and drop the rest to produce the required number of channels. In D, where the number of channels should be increased, BigGAN-deep passes the input channels unperturbed, and concatenates them with the remaining channels produced by a 1 × 1 convolution. As far as the network configuration is concerned, the discriminator is an exact reflection of the generator. There are two blocks at each resolution (BigGAN uses one), and as a result BigGAN-deep is four times deeper than BigGAN. Despite their increased depth, the BigGAN-deep models have significantly fewer parameters mainly due to the bottleneck structure of their residual blocks.

VQ-VAE-2 is a type of variational autoencoder that combines a a two-level hierarchical VQ-VAE with a self-attention autoregressive model (PixelCNN) as a prior. The encoder and decoder architectures are kept simple and light-weight as in the original VQ-VAE, with the only difference that hierarchical multi-scale latent maps are used for increased resolution.

Voxel RoI Pooling is a RoI feature extractor extracts RoI features directly from voxel features for further refinement. It starts by dividing a region proposal into $G \times G \times G$ regular sub-voxels. The center point is taken as the grid point of the corresponding sub-voxel. Since $3 D$ feature volumes are extremely sparse (non-empty voxels account for $<3 \%$ spaces), we cannot directly utilize max pooling over features of each sub-voxel. Instead, features are integrated from neighboring voxels into the grid points for feature extraction. Specifically, given a grid point $g\_{i}$, we first exploit voxel query to group a set of neighboring voxels $\Gamma\_{i}=\left\(\mathbf{v}\_{i}^{1}, \mathbf{v}\_{i}^{2}, \cdots, \mathbf{v}\_{i}^{K}\right\) .$ Then, we aggregate the neighboring voxel features with a PointNet module $\mathrm{a}$ as: $$ \mathbf{\eta}\_{i}=\max _{k=1,2, \cdots, K}\left\(\Psi\left(\left[\mathbf{v}\_{i}^{k}-\mathbf{g}\_{i} ; \mathbf{\phi}\_{i}^{k}\right]\right)\right\) $$ where $\mathbf{v}\_{i}-\mathbf{g}\_{i}$ represents the relative coordinates, $\mathbf{\phi}\_{i}^{k}$ is the voxel feature of $\mathbf{v}\_{i}^{k}$, and $\Psi(\cdot)$ indicates an MLP. The max pooling operation $\max (\cdot)$ is performed along the channels to obtain the aggregated feature vector $\eta_{i} .$ Particularly, Voxel RoI pooling is exploited to extract voxel features from the 3D feature volumes out of the last two stages in the $3 \mathrm{D}$ backbone network. And for each stage, two Manhattan distance thresholds are set to group voxels with multiple scales. Then, we concatenate the aggregated features pooled from different stages and scales to obtain the RoI features.

Dynamic R-CNN is an object detection method that adjusts the label assignment criteria (IoU threshold) and the shape of regression loss function (parameters of Smooth L1 Loss) automatically based on the statistics of proposals during training. The motivation is that in previous two-stage object detectors, there is an inconsistency problem between the fixed network settings and the dynamic training procedure. For example, the fixed label assignment strategy and regression loss function cannot fit the distribution change of proposals and thus are harmful to training high quality detectors. It consists of two components: Dynamic Label Assignment and Dynamic Smooth L1 Loss, which are designed for the classification and regression branches, respectively. For Dynamic Label Assignment, we want our model to be discriminative for high IoU proposals, so we gradually adjust the IoU threshold for positive/negative samples based on the proposals distribution in the training procedure. Specifically, we set the threshold as the IoU of the proposal at a certain percentage since it can reflect the quality of the overall distribution. For Dynamic Smooth L1 Loss, we want to change the shape of the regression loss function to adaptively fit the distribution change of error and ensure the contribution of high quality samples to training. This is achieved by adjusting the $\beta$ in Smooth L1 Loss based on the error distribution of the regression loss function, in which $\beta$ actually controls the magnitude of the gradient of small errors.

Conditional DBlock is a residual based block used in the discriminator of the GAN-TTS architecture. They are similar to the GBlocks used in the generator, but without batch normalization. Unlike the DBlock, the Conditional DBlock adds the embedding of the linguistic features after the first convolution.

DBlock is a residual based block used in the discriminator of the GAN-TTS architecture. They are similar to the GBlocks used in the generator, but without batch normalisation.

GAN-TTS is a generative adversarial network for text-to-speech synthesis. The architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyze the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. The generator architecture consists of several GBlocks, which are residual based (dilated) convolution blocks. GBlocks 3–7 gradually upsample the temporal dimension of hidden representations by factors of 2, 2, 2, 3, 5, while the number of channels is reduced by GBlocks 3, 6 and 7 (by a factor of 2 each). The final convolutional layer with Tanh activation produces a single-channel audio waveform. Instead of a single discriminator, GAN-TTS uses an ensemble of Random Window Discriminators (RWDs) which operate on randomly sub-sampled fragments of the real or generated samples. The ensemble allows for the evaluation of audio in different complementary ways.

GBlock is a type of residual block used in the GAN-TTS text-to-speech architecture - it is a stack of two residual blocks. As the generator is producing raw audio (e.g. a 2s training clip corresponds to a sequence of 48000 samples), dilated convolutions are used to ensure that the receptive field of $G$ is large enough to capture long-term dependencies. The four kernel size-3 convolutions in each GBlock have increasing dilation factors: 1, 2, 4, 8. Convolutions are preceded by Conditional Batch Normalisation, conditioned on the linear embeddings of the noise term $z \sim N\left(0, \mathbf{I}\_{128}\right)$ in the single-speaker case, or the concatenation of $z$ and a one-hot representation of the speaker ID in the multi-speaker case. The embeddings are different for each BatchNorm instance. A GBlock contains two skip connections, the first of which in GAN-TTS performs upsampling if the output frequency is higher than the input, and it also contains a size-1 convolution if the number of output channels is different from the input.

Multiple Random Window Discriminator is a discriminator used for the GAN-TTS text-to-speech architecture. These discriminators operate on randomly sub-sampled fragments of the real or generated samples. The ensemble allows for the evaluation of audio in different complementary ways, and is obtained by taking a Cartesian product of two parameter spaces: (i) the size of the random windows fed into the discriminator; (ii) whether a discriminator is conditioned on linguistic and pitch features. For example, in the authors' best-performing model, they consider five window sizes (240, 480, 960, 1920, 3600 samples), which yields 10 discriminators in total. Using random windows of different size, rather than the full generated sample, has a data augmentation effect and also reduces the computational complexity of RWDs. In the first layer of each discriminator, the MRWD reshapes (downsamples) the input raw waveform to a constant temporal dimension $\omega = 240$ by moving consecutive blocks of samples into the channel dimension, i.e. from $\left[\omega\_{k}, 1\right]$ to $\left[\omega, k\right]$, where $k$ is the downsampling factor (e.g. $k = 8$ for input window size $1920$). This way, all the RWDs have the same architecture and similar computational complexity despite different window sizes. The conditional discriminators have access to linguistic and pitch features, and can measure whether the generated audio matches the input conditioning. This means that random windows in conditional discriminators need to be aligned with the conditioning frequency to preserve the correspondence between the waveform and linguistic features within the sampled window. This limits the valid sampling to that of the frequency of the conditioning signal (200Hz, or every 5ms). The unconditional discriminators, on the contrary, only evaluate whether the generated audio sounds realistic regardless of the conditioning. The random windows for these discriminators are sampled without constraints at full 24kHz frequency, which further increases the amount of training data. For the architecture, the discriminators consists of blocks (DBlocks) that are similar to the GBlocks used in the generator, but without batch normalisation. Unconditional RWDs are composed entirely of DBlocks. In conditional RWDs, the input waveform is gradually downsampled by DBlocks, until the temporal dimension of the activation is equal to that of the conditioning, at which point a conditional DBlock is used. This joint information is then passed to the remaining DBlocks, whose final output is average-pooled to obtain a scalar. The dilation factors in the DBlocks’ convolutions follow the pattern 1, 2, 1, 2 – unlike the generator, the discriminator operates on a relatively small window, and the authors did not observe any benefit from using larger dilation factors.

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