10-shot image generation

A Depthwise Fire Module is a modification of a Fire Module with depthwise separable convolutions to improve the inference time performance. It is used in the CornerNet-Lite architecture for object detection.

DetNASNet is a convolutional neural network designed to be an object detection backbone and discovered through DetNAS architecture search. It uses ShuffleNet V2 blocks as its basic building block.

A DiCE Unit is an image model block that is built using dimension-wise convolutions and dimension-wise fusion. The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations; allowing the DiCE unit to efficiently encode spatial and channel-wise information contained in the input tensor. Standard convolutions encode spatial and channel-wise information simultaneously, but they are computationally expensive. To improve the efficiency of standard convolutions, separable convolution are introduced, where spatial and channelwise information are encoded separately using depth-wise and point-wise convolutions, respectively. Though this factorization is effective, it puts a significant computational load on point-wise convolutions and makes them a computational bottleneck. DiCE Units utilize a dimension-wise convolution to encode depth-wise, width-wise, and height-wise information independently. The dimension-wise convolutions encode local information from different dimensions of the input tensor, but do not capture global information. One approach is a pointwise convolution, but it is computationally expensive, so instead dimension-wise fusion factorizes the point-wise convolution in two steps: (1) local fusion and (2) global fusion.

DiCENet is a convolutional neural network architecture that utilizes dimensional convolutions (and dimension-wise fusion). The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations; allowing the DiCE Unit in the network to efficiently encode spatial and channel-wise information contained in the input tensor.

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ERNIE-GEN is a multi-flow sequence to sequence pre-training and fine-tuning framework which bridges the discrepancy between training and inference with an infilling generation mechanism and a noise-aware generation method. To make generation closer to human writing patterns, this framework introduces a span-by-span generation flow that trains the model to predict semantically-complete spans consecutively rather than predicting word by word. Unlike existing pre-training methods, ERNIE-GEN incorporates multi-granularity target sampling to construct pre-training data, which enhances the correlation between encoder and decoder.

ESPNetv2 is a convolutional neural network that utilises group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters.

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Decoder architecture inspired on the UNet++ structure and the EfficientNet building blocks. Keeping the UNet++ structure, the EfficientUNet++ achieves higher performance and significantly lower computational complexity through two simple modifications: * Replaces the 3x3 convolutions of the UNet++ with residual bottleneck blocks with depthwise convolutions * Applies channel and spatial attention to the bottleneck feature maps using concurrent spatial and channel squeeze & excitation (scSE) blocks

An Elastic ResNeXt Block is a modification of the ResNeXt Block that adds downsamplings and upsamplings in parallel branches at each layer. It is called "elastic" because each layer in the network is flexible in terms of choosing the best scale by a soft policy.

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FastSpeech 2s is a text-to-speech model that abandons mel-spectrograms as intermediate output completely and directly generates speech waveform from text during inference. In other words there is no cascaded mel-spectrogram generation (acoustic model) and waveform generation (vocoder). FastSpeech 2s generates waveform conditioning on intermediate hidden, which makes it more compact in inference by discarding the mel-spectrogram decoder. Two main design changes are made to the waveform decoder. First, considering that the phase information is difficult to predict using a variance predictor, adversarial training is used in the waveform decoder to force it to implicitly recover the phase information by itself. Secondly, the mel-spectrogram decoder of FastSpeech 2 is leveraged, which is trained on the full text sequence to help on the text feature extraction. As shown in the Figure, the waveform decoder is based on the structure of WaveNet including non-causal convolutions and gated activation. The waveform decoder takes a sliced hidden sequence corresponding to a short audio clip as input and upsamples it with transposed 1D-convolution to match the length of audio clip. The discriminator in the adversarial training adopts the same structure in Parallel WaveGAN, which consists of ten layers of non-causal dilated 1-D convolutions with leaky ReLU activation function. The waveform decoder is optimized by the multi-resolution STFT loss and the LSGAN discriminator loss following Parallel WaveGAN. In inference, the mel-spectrogram decoder is discarded and only the waveform decoder is used to synthesize speech audio.

Feature Intertwiner is an object detection module that leverages the features from a more reliable set to help guide the feature learning of another less reliable set. The mutual learning process helps two sets to have closer distance within the cluster in each class. The intertwiner is applied on the object detection task, where a historical buffer is proposed to address the sample missing problem during one mini-batch and the optimal transport (OT) theory is introduced to enforce the similarity among the two sets.

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Classifier guided sampling from diffusion models involves the computation of gradients of classifier probabilities, and this is computationally expensive as it involves the use of autograd operators. To mitigate this issue, Classifier-Free Guidance (CFG), was proposed to use an unconditional sample as a reference to increase contrast from. We extend these concepts to devise a novel formulation that utilizes a classifier, without computation of gradients, to generate an conditional sample as the reference. In what follows, we describe this methodology and refer to our method as “gradient-free classifier guidance” (GFCG). Our method is also adaptive in that it computes the guidance scale on the-fly depending on how confused the diffusion model is during the denoising process.

Group Decreasing Network, or GroupDNet, is a type of convolutional neural network for multi-modal image synthesis. GroupDNet contains one encoder and one decoder. Inspired by the idea of VAE and SPADE, the encoder $E$ produces a latent code $Z$ that is supposed to follow a Gaussian distribution $\mathcal{N}(0,1)$ during training. While testing, the encoder $E$ is discarded. A randomly sampled code from the Gaussian distribution substitutes for $Z$. To fulfill this, the re-parameterization trick is used to enable a differentiable loss function during training. Specifically, the encoder predicts a mean vector and a variance vector through two fully connected layers to represent the encoded distribution. The gap between the encoded distribution and Gaussian distribution can be minimized by imposing a KL-divergence loss.

HS-ResNet is a convolutional neural network that employs Hierarchical-Split Block as its central building block within a ResNet-like architecture.

A Harmonic Network, or Harm-Net, is a type of convolutional neural network that replaces convolutional layers with "harmonic blocks" that use Discrete Cosine Transform (DCT) filters. These blocks can be useful in truncating high-frequency information (possible due to the redundancies in the spectral domain).

Hierarchical-Split Block is a representational block for multi-scale feature representations. It contains many hierarchical split and concatenate connections within one single residual block. Specifically, ordinary feature maps in deep neural networks are split into $s$ groups, each with $w$ channels. As shown in the Figure, only the first group of filters can be straightly connected to next layer. The second group of feature maps are sent to a convolution of $3 \times 3$ filters to extract features firstly, then the output feature maps are split into two sub-groups in the channel dimension. One sub-group of feature maps straightly connected to next layer, while the other sub-group is concatenated with the next group of input feature maps in the channel dimension. The concatenated feature maps are operated by a set of $3 \times 3$ convolutional filters. This process repeats several times until the rest of input feature maps are processed. Finally, features maps from all input groups are concatenated and sent to another layer of $1 \times 1$ filters to rebuild the features.

Hit-Detector is a neural architectures search algorithm that simultaneously searches all components of an object detector in an end-to-end manner. It is a hierarchical approach to mine the proper subsearch space from the large volume of operation candidates. It consists of two main procedures. First, given a large search space containing all the operation candidates, we screen out the customized sub search space suitable for each part of detector with the help of group sparsity regularization. Secondly, we search the architectures for each part within the corresponding sub search space by adopting the differentiable manner.

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