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CoLU is an activation function similar to Swish and Mish in properties. It is defined as: $$f(x)=\frac{x}{1-x^{-(x+e^x)}}$$ It is smooth, continuously differentiable, unbounded above, bounded below, non-saturating, and non-monotonic. Based on experiments done with CoLU with different activation functions, it is observed that CoLU usually performs better than other functions on deeper neural networks.

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Colorization Transformer is a probabilistic colorization model composed only of axial self-attention blocks. The main advantages of these blocks are the ability to capture a global receptive field with only two layers and $\mathcal{O}(D\sqrt{D})$ instead of $\text{O}(D^{2})$ complexity. In order to enable colorization of high-resolution grayscale images, the task is decomposed into three simpler sequential subtasks: coarse low resolution autoregressive colorization, parallel color and spatial super-resolution. For coarse low resolution colorization, a conditional variant of Axial Transformer is applied. The authors leverage the semi-parallel sampling mechanism of Axial Transformers. Finally, fast parallel deterministic upsampling models are employed to super-resolve the coarsely colorized image into the final high resolution output.

A Compact Global Descriptor is an image model block for modelling interactions between positions across different dimensions (e.g., channels, frames). This descriptor enables subsequent convolutions to access the informative global features. It is a form of attention.

ComplEx model trained with a nuclear norm regularizer; A relation prediction objective is added on top of the commonly used 1vsAll objective.

Concatenation Affinity is a type of affinity or self-similarity function between two points $\mathbb{x\_{i}}$ and $\mathbb{x\_{j}}$ that uses a concatenation function: $$ f\left(\mathbb{x\_{i}}, \mathbb{x\_{j}}\right) = \text{ReLU}\left(\mathbb{w}^{T}\_{f}\left[\theta\left(\mathbb{x}\_{i}\right), \phi\left(\mathbb{x}\_{j}\right)\right]\right)$$ Here $\left[·, ·\right]$ denotes concatenation and $\mathbb{w}\_{f}$ is a weight vector that projects the concatenated vector to a scalar.

Given a corrupted input image, Con\textit{ffusion}, repurposes a pretrained diffusion model to generate lower and upper bounds around each reconstructed pixel. The true pixel value is guaranteed to fall within these bounds with probability $p$.

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The Content-Conditioned Style Encoder, or COCO, is a style encoder used for image-to-image translation in the COCO-FUNIT architecture. Unlike the style encoder in FUNIT, COCO takes both content and style image as input. With this content conditioning scheme, we create a direct feedback path during learning to let the content image influence how the style code is computed. It also helps reduce the direct influence of the style image to the extract style code. The bottom part of the Figure details architecture. First, the content image is fed into an encoder $E\_{S, C}$ to compute a spatial feature map. This content feature map is then mean-pooled and mapped to a vector $\zeta\_{c} .$ Similarly, the style image is fed into encoder $E\_{S, S}$ to compute a spatial feature map. The style feature map is then mean-pooled and concatenated with an input-independent bias vector: the constant style bias (CSB). Note that while the regular bias in deep networks is added to the activations, in CSB, the bias is concatenated with the activations. The CSB provides a fixed input to the style encoder, which helps compute a style code that is less sensitive to the variations in the style image. The concatenation of the style vector and the CSB is mapped to a vector $\zeta\_{s}$ via a fully connected layer. We then perform an element-wise product operation to $\zeta\_{c}$ and $\zeta\_{s}$, which is the final style code. The style code is then mapped to produce the AdaIN parameters for generating the translation. Through this element-wise product operation, the resulting style code is heavily influenced by the content image. One way to look at this mechanism is that it produces a customized style code for the input content image. The COCO is used as a drop-in replacement for the style encoder in FUNIT. Let $\phi$ denote the COCO mapping. The translation output is then computed via $$ z\_{c}=E\_{c}\left(x_{c}\right), z_{s}=\phi\left(E\_{s, s}\left(x_{s}\right), E\_{s, c}\left(x\_{c}\right)\right), \overline{\mathbf{x}}=F\left(z\_{c}, z\_{s}\right) $$ The style code extracted by the COCO is more robust to variations in the style image. Note that we set $E\_{S, C} \equiv E\_{C}$ to keep the number of parameters in our model similar to that in FUNIT.

Contextual Residual Aggregation, or CRA, is a module for image inpainting. It can produce high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Specifically, it involves a neural network to predict a low-resolution inpainted result and up-sample it to yield a large blurry image. Then we produce the high-frequency residuals for in-hole patches by aggregating weighted high-frequency residuals from contextual patches. Finally, we add the aggregated residuals to the large blurry image to obtain a sharp result.

CornerNet-Saccade is an extension of CornerNet with an attention mechanism similar to saccades in human vision. It starts with a downsized full image and generates an attention map, which is then zoomed in on and processed further by the model. This differs from the original CornerNet in that it is applied fully convolutionally across multiple scales.

CornerNet-Squeeze Hourglass is a convolutional neural network and object detection backbone used in the CornerNet-Squeeze object detector. It uses a modified hourglass module that makes use of a fire module: containing 1x1 convolutions and depthwise convolutions.

CornerNet-Squeeze Hourglass Module is an image model block used in CornerNet-Lite that is based on an hourglass module, but uses modified fire modules instead of residual blocks. Other than replacing the residual blocks, further modifications include: reducing the maximum feature map resolution of the hourglass modules by adding one more downsampling layer before the hourglass modules, removing one downsampling layer in each hourglass module, replacing the 3 × 3 filters with 1 x 1 filters in the prediction modules of CornerNet, and finally replacing the nearest neighbor upsampling in the hourglass network with transpose convolution with a 4 × 4 kernel.

The Cosine Linear Unit, or CosLU, is a type of activation function that has trainable parameters and uses the cosine function. $$CosLU(x) = (x + \alpha \cos(\beta x))\sigma(x)$$

Our model known as CubeRE first encodes each input sentence using a language model encoder to obtain the contextualized sequence representation. We then capture the interaction between each possible head and tail entity as a pair representation for predicting the entity-relation label scores. To reduce the computational cost, each sentence is pruned to retain only words that have higher entity scores. Finally, we capture the interaction between each possible relation triplet and qualifier to predict the qualifier label scores and decode the outputs.

CutBlur is a data augmentation method that is specifically designed for the low-level vision tasks. It cuts a low-resolution patch and pastes it to the corresponding high-resolution image region and vice versa. The key intuition of Cutblur is to enable a model to learn not only "how" but also "where" to super-resolve an image. By doing so, the model can understand "how much" instead of blindly learning to apply super-resolution to every given pixel.

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Distributed Any-Batch Mirror Descent (DABMD) is based on distributed Mirror Descent but uses a fixed per-round computing time to limit the waiting by fast nodes to receive information updates from slow nodes. DABMD is characterized by varying minibatch sizes across nodes. It is applicable to a broader range of problems compared with existing distributed online optimization methods such as those based on dual averaging, and it accommodates time-varying network topology.

Domain Adaptive Ensemble Learning, or DAEL, is an architecture for domain adaptation. The model is composed of a CNN feature extractor shared across domains and multiple classifier heads each trained to specialize in a particular source domain. Each such classifier is an expert to its own domain and a non-expert to others. DAEL aims to learn these experts collaboratively so that when forming an ensemble, they can leverage complementary information from each other to be more effective for an unseen target domain. To this end, each source domain is used in turn as a pseudo-target-domain with its own expert providing supervisory signal to the ensemble of non-experts learned from the other sources. For unlabeled target data under the UDA setting where real expert does not exist, DAEL uses pseudo-label to supervise the ensemble learning.

A brand new Foundation Model framework for deep learning in the future.

DASPP is a deeper version of the ASPP module (the latter from DeepLabv3) that adds standard 3 × 3 convolution after 3 × 3 dilated convolutions to refine the features and also fusing the input and the output of the DASPP module via short residual connection. Also, the number of convolution filters of ASPP is reduced from 255 to 96 to gain computational performance.

DDParser, or Baidu Dependency Parser, is a Chinese dependency parser trained on a large-scale manually labeled dataset called Baidu Chinese Treebank (DuCTB). For inputs, for the $i$ th word, its input vector $e_{i}$ is the concatenation of the word embedding and character-level representation: $$ e\_{i}=e\_{i}^{w o r d} \oplus C h a r L S T M\left(w\_{i}\right) $$ Where $\operatorname{CharLSTM}\left(w_{i}\right)$ is the output vectors after feeding the character sequence into a BiLSTM layer. The experimental results on DuCTB dataset show that replacing POS tag embeddings with $\operatorname{CharLSTM}\left(w_{i}\right)$ leads to the improvement. For the BiLSTM encoder, three BiLSTM layers are employed over the input vectors for context encoding. Denote $r\_{i}$ the output vector of the top-layer BiLSTM for $w\_{i}$ The dependency parser of Dozat and Manning is used. Dimension-reducing MLPs are applied to each recurrent output vector $r\_{i}$ before applying the biaffine transformation. Applying smaller MLPs to the recurrent output states before the biaffine classifier has the advantage of stripping away information not relevant to the current decision. Then biaffine attention is used both in the dependency arc classifier and relation classifier. The computations of all symbols in the Figure are shown below: $$ h_{i}^{d-a r c}=M L P^{d-a r c}\left(r_{i}\right) $$ $$ h_{i}^{h-a r c}=M L P^{h-a r c}\left(r_{i}\right) \\ $$ $$ h_{i}^{d-r e l}=M L P^{d-r e l}\left(r_{i}\right) \\ $$ $$ h_{i}^{h-r e l}=M L P^{h-r e l}\left(r_{i}\right) \\ $$ $$ S^{a r c}=\left(H^{d-a r c} \oplus I\right) U^{a r c} H^{h-a r c} \\ $$ $$ S^{r e l}=\left(H^{d-r e l} \oplus I\right) U^{r e l}\left(\left(H^{h-r e l}\right)^{T} \oplus I\right)^{T} $$ For the decoder, the first-order Eisner algorithm is used to ensure that the output is a projection tree. Based on the dependency tree built by biaffine parser, we get a word sequence through the in-order traversal of the tree. The output is a projection tree only if the word sequence is in order.

DExTra, or Deep and Light-weight Expand-reduce Transformation, is a light-weight expand-reduce transformation that enables learning wider representations efficiently. DExTra maps a $d\_{m}$ dimensional input vector into a high dimensional space (expansion) and then reduces it down to a $d\_{o}$ dimensional output vector (reduction) using $N$ layers of group transformations. During these expansion and reduction phases, DExTra uses group linear transformations because they learn local representations by deriving the output from a specific part of the input and are more efficient than linear transformations. To learn global representations, DExTra shares information between different groups in the group linear transformation using feature shuffling Formally, the DExTra transformation is controlled by five configuration parameters: (1) depth $N$, (2) width multiplier $m\_{w}$, (3) input dimension $d\_{m}$, (4) output dimension $d\_{o}$, and (5) maximum groups $g\_{max}$ in a group linear transformation. In the expansion phase, DExTra projects the $d\_{m}$-dimensional input to a high-dimensional space, $d\_{max} = m\_{w}d\_{m}$, linearly using $\text{ceil}\left(\frac{N}{2}\right)$ layers. In the reduction phase, DExTra projects the $d\_{max}$-dimensional vector to a $d\_{o}$-dimensional space using the remaining $N -\text{ceil}\left(\frac{N}{2}\right)$ layers. Mathematically, we define the output $Y$ at each layer $l$ as: $$ \mathbf{Y}\_{l} = \mathcal{F}\left(\mathbf{X}, \mathbf{W}^{l}, \mathbf{b}^{l}, g^{l}\right) \text{ if } l=1 $$ $$ \mathbf{Y}\_{l} = \mathcal{F}\left(\mathcal{H}\left(\mathbf{X}, \mathbf{Y}^{l-1}\right), \mathbf{W}^{l}, \mathbf{b}^{l}, g^{l}\right) \text{ Otherwise } $$ where the number of groups at each layer $l$ are computed as: $$ g^{l} = \text{min}\left(2^{l-1}, g\_{max}\right), 1 \leq l \leq \text{ceil}\left(N/2\right) $$ $$ g^{N-l}, \text{Otherwise}$$ In the above equations, $\mathcal{F}$ is a group linear transformation function. The function $\mathcal{F}$ takes the input $\left(\mathbf{X} \text{ or } \mathcal{H}\left(\mathbf{X}, \mathbf{Y}^{l-1}\right) \right)$, splits it into $g^{l}$ groups, and then applies a linear transformation with learnable parameters $\mathbf{W}^{l}$ and bias $\mathbf{b}^{l}$ to each group independently. The outputs of each group are then concatenated to produce the final output $\mathbf{Y}^{l}$. The function $\mathcal{H}$ first shuffles the output of each group in $\mathbf{Y}^{l−1}$ and then combines it with the input $\mathbf{X}$ using an input mixer connection. In the authors' experiments, they use $g\_{max} = \text{ceil}\left(\frac{d\_{m}}{32}\right)$ so that each group has at least 32 input elements. Note that (i) group linear transformations reduce to linear transformations when $g^{l} = 1$, and (ii) DExTra is equivalent to a multi-layer perceptron when $g\_{max} = 1$.

DFDNet, or DFDNet, is a deep face dictionary network for face restoration to guide the restoration process of degraded observations. Given a LQ image $I\_{d}$, the DFDNet selects the dictionary features that have the most similar structure with the input. Specially, we re-norm the whole dictionaries via component AdaIN (termed as CAdaIN) based on the input component to eliminate the distribution or style diversity. The selected dictionary features are then utilized to guide the restoration process via dictionary feature transformation.

InfoNCE variants demonstrate direct and indirect coupling between the alignment and uniformity terms thus hurting optimisation. The Decoupled Hyperspherical Energy Loss (DHEL) is an NT-Xent variant that completly decouples alignment from uniformity by discarding the corresponding terms from the denominator. In this way optimisation is more efficient and robust to hyper parameter changes.

Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space. Existing methods concentrate on learning latent representation via reconstruction tasks, but cannot directly optimize representation and are prone to oversmoothing, thus limiting the applications on downstream tasks. To alleviate these issues, we propose a novel graph embedding framework named Deep Manifold Attributed Graph Embedding (DMAGE). A node-to-node geodesic similarity is proposed to compute the inter-node similarity between the data space and the latent space and then use Bergman divergence as loss function to minimize the difference between them. We then design a new network structure with fewer aggregation to alleviate the oversmoothing problem and incorporate graph structure augmentation to improve the representation's stability. Our proposed DMAGE surpasses state-of-the-art methods by a significant margin on three downstream tasks: unsupervised visualization, node clustering, and link prediction across four popular datasets.

Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their local features. Previous learning-based studies mainly focus on either global or local image representation learning to tackle the retrieval task. In this paper, we abandon the two-stage paradigm and seek to design an effective singlestage solution by integrating local and global information inside images into compact image representations. Specifically, we propose a Deep Orthogonal Local and Global (DOLG) information fusion framework for end-to-end image retrieval. It attentively extracts representative local information with multi-atrous convolutions and self-attention at first. Components orthogonal to the global image representation are then extracted from the local information. At last, the orthogonal components are concatenated with the global representation as a complementary, and then aggregation is performed to generate the final representation. The whole framework is end-to-end differentiable and can be trained with image-level labels. Extensive experimental results validate the effectiveness of our solution and show that our model achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets.

Parameter tuning method for neural network models with adaptive activation functions.

A method to determine the dataset size used to LoRA fine-tune a model based on its weights by extracting the singular values of each LoRA matrix and training layer-specific nearest-neighbor classifiers.

DU-GAN is a generative adversarial network for LDCT denoising in medical imaging. The generator produces denoised LDCT images, and two independent branches with U-Net based discriminators perform at the image and gradient domains. The U-Net based discriminator provides both global structure and local per-pixel feedback to the generator. Furthermore, the image discriminator encourages the generator to produce photo-realistic CT images while the gradient discriminator is utilized for better edge and alleviating streak artifacts caused by photon starvation.

DVD-GAN is a generative adversarial network for video generation built upon the BigGAN architecture. DVD-GAN uses two discriminators: a Spatial Discriminator $\mathcal{D}\_{S}$ and a Temporal Discriminator $\mathcal{D}\_{T}$. $\mathcal{D}\_{S}$ critiques single frame content and structure by randomly sampling $k$ full-resolution frames and judging them individually. The temporal discriminator $\mathcal{D}\_{T}$ must provide $G$ with the learning signal to generate movement (not evaluated by $\mathcal{D}\_{S}$). The input to $G$ consists of a Gaussian latent noise $z \sim N\left(0, I\right)$ and a learned linear embedding $e\left(y\right)$ of the desired class $y$. Both inputs are 120-dimensional vectors. $G$ starts by computing an affine transformation of $\left[z; e\left(y\right)\right]$ to a $\left[4, 4, ch\_{0}\right]$-shaped tensor. $\left[z; e\left(y\right)\right]$ is used as the input to all class-conditional Batch Normalization layers throughout $G$. This is then treated as the input (at each frame we would like to generate) to a Convolutional GRU. This RNN is unrolled once per frame. The output of this RNN is processed by two residual blocks. The time dimension is combined with the batch dimension here, so each frame proceeds through the blocks independently. The output of these blocks has width and height dimensions which are doubled (we skip upsampling in the first block). This is repeated a number of times, with the output of one RNN + residual group fed as the input to the next group, until the output tensors have the desired spatial dimensions. The spatial discriminator $\mathcal{D}\_{S}$ functions almost identically to BigGAN’s discriminator. A score is calculated for each of the uniformly sampled $k$ frames (default $k = 8$) and the $\mathcal{D}\_{S}$ output is the sum over per-frame scores. The temporal discriminator $\mathcal{D}\_{T}$ has a similar architecture, but pre-processes the real or generated video with a $2 \times 2$ average-pooling downsampling function $\phi$. Furthermore, the first two residual blocks of $\mathcal{D}\_{T}$ are 3-D, where every convolution is replaced with a 3-D convolution with a kernel size of $3 \times 3 \times 3$. The rest of the architecture follows BigGAN.

DeLiGHT is a transformer architecture that delivers parameter efficiency improvements by (1) within each Transformer block using DExTra, a deep and light-weight transformation, allowing for the use of single-headed attention and bottleneck FFN layers and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output.