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CDCC-NET is a multi-task network that analyzes the detected counter region and predicts 9 outputs: eight float numbers referring to the corner positions (x0/w, y0/h, ... , x3/w, y3/h) and an array containing two float numbers regarding the probability of the counter being legible/operational or illegible/faulty.

Characterizable Invertible $3\times3$ Convolution

CNN-TS: A Deep Convolutional Neural Network for Time Series Classification This method leverages deep convolutional neural networks to classify time series data with intermediate targets. It is designed to improve accuracy in time series analysis.

COCO-FUNIT is few-shot image translation model which computes the style embedding of the example images conditioned on the input image and a new module called the constant style bias. It builds on top of FUNIT by identifying the content loss problem and then addressing it with a novel content-conditioned style encoder architecture. The FUNIT method suffers from the content loss problem—the translation result is not well-aligned with the input image. While a direct theoretical analysis is likely elusive, we conduct an empirical study, aiming at identify the cause of the content loss problem. In analyses, the authors show that the FUNIT style encoder produces very different style codes using different crops -- suggesting the style code contains other information about the style image such as the object pose. To make the style embedding more robust to small variations in the style image, a new style encoder architecture, the Content-Conditioned style encoder (COCO), is introduced. The most distinctive feature of this new encoder is the conditioning in the content image as illustrated in the top-right of the Figure. Unlike the style encoder in FUNIT, COCO takes both content and style image as input. With this content-conditioning scheme, a direct feedback path is created 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.

COP-KMeans is a modified version the popular k-means algorithm that supports pairwise constraints. Original paper : Constrained K-means Clustering with Background Knowledge, Wagstaff et al. 2001

CP with N3 Regularizer

CP with N3 Regularizer and Relation Prediction

Contrastive Predictive Coding v2 (CPC v2) is a self-supervised learning approach that builds upon the original CPC with several improvements. These improvements include: - Model capacity - The third residual stack of ResNet-101 (originally containing 23 blocks, 1024-dimensional feature maps, and 256-dimensional bottleneck layers), is converted to use 46 blocks, with 4096-dimensional feature maps and 512-dimensional bottleneck layers: ResNet-161. - Layer Normalization - The authors find CPC with batch normalization harms downstream performance. They hypothesize this is due to batch normalization allowing large models to find a trivial solution to CPC: it introduces a dependency between patches (through the batch statistics) that can be exploited to bypass the constraints on the receptive field. They replace batch normalization with layer normalization. - Predicting lengths and directions - patches are predicted with contexts from both directions rather than just spatially underneath. - Patch-based Augmentation - Utilising "color dropping" which randomly drops two of the three color channels in each patch, as well as random horizontal flips. Consistent with prior results, this new architecture delivers better performance regardless of

CRTracker is an end-to-end cross-view referring multi-object tracking method.

CSPPeleeNet is a convolutional neural network and object detection backbone where we apply the Cross Stage Partial Network (CSPNet) approach to PeleeNet. The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.

TL;DR: CT-Layer is a GNN Layer which is able to rewire a graph in an inductive an parameter-free way according to the commute times distance (or effective resistance). We address it learning a differentiable way to compute the CT-embedding of the graph. ### Summary CT-Layer is able to Learn the Commute Times distance between nodes (i.e. effective resistance distance) in a differentiable way, instead of the common spectral version, and in a parameter free manner, which is not the cased of the heat kernel. This approach allow to solve it as an optimization problem inside a GNN, leading to have a new layer which is able to learn how rewire a given graph in an optimal, and inductive way. In addition, CT-Layer also is able to learn Commute Times embeddings, and then calculate it for any graph in an inductive way. The Commute Times embedding is also related with the eigenvalues and eigenvectors of the Laplacian of the graph, because CT embedding is just the eigenvectors scaled. Therefore, CT-Layer is also able to learn hot to calculate the spectrum of the Laplacian in a differentiable way. Therefore, this embedding must satisfy orthogonality and normality. Finally, recent connections has been found between commute times distance and curvature (which is non-differentiable), establishing equivalences between them. Therefore, CT-Layer can also be seen as the differentiable version of the curvature rewiring. **We are going through a quick overview of the layer, but I suggest go to the paper for a detailed explanation. ** ### Spectral CT- Embedding downsides CT-embedding $\mathbf{Z}$ is computed spectrally in the literature (until the proposal of this method) or it is approximated using the heat kernel (very dependent on hyperparameter $t$). This fact does not allow us to propose differentiable methods using that measure: $$ \mathbf{Z}=\sqrt{vol(G)}\mathbf{\Lambda}^\frac{1}{2}\mathbf{F}^T \textrm{ given } \mathbf{L}=\mathbf{F}\mathbf{\Lambda}\mathbf{F}^T $$ Then, CT-distance is given by the Euclidean distances between the embeddings $CT_{uv} = ||\mathbf{z_u}-\mathbf{z_v}||^2$. The spectral form is: $$ \frac{CT_{uv}}{vol(G)} = \sum_{i=2}^n \frac{1}{\lambda_i} (\mathbf{f}(u)-\mathbf{f}(v))^2 $$ being $\mathbf{f}$ the eigenvectors of the graph Laplacian. This embedding and distances gives us desirable properties of the graph, such an understanding of the structure, or an embedding based on the spectrum which minimizes Dirichlet energies. However, the spectral computation is not differentiable. ### CT-Layer as an optimization problem: Differentiable, learnable and inductive CT-Layer Giving that $\mathbf{Z}$ minimizes Dirichlet energies s.t. being orthogonal and normalized, we can formulate this problem as constraining neighboring nodes to have a similar embeddings s.t. $\mathbf{Z}\mathbf{Z}^T=\mathbf{I}$. $$ \mathbf{Z} = \arg\min_{\mathbf{Z}^T\mathbf{Z}=\mathbf{I}} \frac{\sum\_{u,v} ||\mathbf{z_u}-\mathbf{z_v}||^2\mathbf{A}\_{uv}}{\sum\_{u,v} \mathbf{Z}^2\_{uv} d_u}=\frac{Tr[\mathbf{Z}^T\mathbf{L}\mathbf{Z}]}{Tr[\mathbf{Z}^T\mathbf{D}\mathbf{Z}]} $$ With the above elements we have a definition of CT-Layer, our rewiring layer: Given the matrix $\mathbf{X}\_{n\times F}$ encoding the features of the nodes after any message passing (MP) layer, $\mathbf{Z}\_{n\times O(n)}=\tanh(\textrm{MLP}(\mathbf{X}))$ learns the association $\mathbf{X}\rightarrow \mathbf{Z}$ while $\mathbf{Z}$ is optimized according to the loss $$ L\_{CT} = \frac{Tr[\mathbf{Z}^T\mathbf{L}\mathbf{Z}]}{Tr[\mathbf{Z}^T\mathbf{D}\mathbf{Z}]} + \left\|\frac{\mathbf{Z}^T\mathbf{Z}}{\|\mathbf{Z}^T\mathbf{Z}\|\_F} - \mathbf{I}\_n\right\|\_F $$ This results in the following resistance diffusion $\mathbf{T}^{CT} = \mathbf{R}(\mathbf{S})\odot \mathbf{A}$ (Hadamard product between the resistance distance and the adjacency) which provides as input to the subsequent MP layer a learnt convolution matrix. As explained before, $\mathbf{Z}$ is the commute times embedding matrix and the pairwise euclidian distance of that learned embeddings are the commute times distances or resistance distances. Therefore, once trained this layer, it will be able to calculate the commute times embedding for a new graph, and rewire that new and unseen graph in a principled way based on the commute times distance. ## Preservation of Structure Does this rewiring preserve the original structure? Let $G' = \textrm{Sparsify}(G, q)$ be a sampling algorithm of graph $G = (V, E)$, where edges $e \in E$ are sampled with probability $q\propto R_e$ (proportional to the effective resistance, i.e. commute times). Then, for $n = |V|$ sufficiently large and $1/\sqrt{n}< \epsilon\le 1$, we need O(n\log n/\epsilon^2)$ samples to satisfy: $$ \forall \mathbf{x}\in\mathbb{R}^n:\; (1-\epsilon)\mathbf{x}^T\mathbf{L}\_G\mathbf{x}\le\mathbf{x}^T\mathbf{L}\_{G'}\mathbf{x}\le (1+\epsilon)\mathbf{x}^T\mathbf{L}\_G\mathbf{x} $$ The intuitions behind is that Dirichlet energies in $G'$ are bounded in $(1\pm \epsilon)$ of the Dirichlet energies of the original graph $G$.

Chained-Tracker, or CTracker, is an online model for multiple-object tracking. It chains paired bounding boxes regression results estimated from overlapping nodes, of which each node covers two adjacent frames. The paired regression is made attentive by object-attention (brought by a detection module) and identity-attention (ensured by an ID verification module). The joint attention module guides the paired boxes regression branch to focus on informative spatial regions with two other branches. One is the object classification branch, which predicts the confidence scores for the first box in the detected box pairs, and such scores are used to guide the regression branch to focus on the foreground regions. The other one is the ID verification branch whose prediction facilitates the regression branch to focus on regions corresponding to the same target. Finally, the bounding box pairs are filtered according to the classification confidence. Then, the generated box pairs belonging to the adjacent frame pairs could be associated using simple methods like IoU (Intersection over Union) matching according to their boxes in the common frame. In this way, the tracking process could be achieved by chaining all the adjacent frame pairs (i.e. chain nodes) sequentially.

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A closed-loop evaluation procedure is first used in a simulator to identify training data samples that are important for practical driving performance and then we these samples to help debias the policy network.

Calculation of Price Graphs Reversal Point Using Average Regression Lines Method Abstract Forecasting methods from averaging regression analysis lines, reversed and direct lines. With both lines intercepting laid the turning points in direction of original line. The average regression method. Keywords: average regression

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\emph{Canonical partition} $\mathcal{P}$ crops the index-restricted d-hop neighborhood around the center node from the target graph. $\mathcal{D}(G_t,v_i,v_c)$ means the shortest distance between $v_i$ and $v_c$ on $G_t$. \begin{equation} \mathcal{P}(G_t, v_c, d) = G_c, \operatorname{ s.t. } G_c \subseteq G_t, V_c = { v_i \in V_t|\mathcal{D}(G_t,v_i,v_c) \leq d , v_i \leq v_c} \end{equation} The graph $G_c$ obtained by canonical partition is called the \emph{canonical neighborhood}. Canonical neighborhoods can correctly substitute the target graph in canonical count. The subgraph count of query in target equals the summation of the canonical count of query in canonical neighborhoods for all target nodes. Canonical neighborhoods are acquired with canonical partition $\mathcal{P}$, given any $d$ greater than the diameter of the query. \begin{equation} \mathcal{C}(G_q,G_t) = \sum_{v_c \in V_t} \mathcal{C}c(G_q, \mathcal{P}(G_t, v_c, d),v_c), d \geq \max{v_i, v_j \in V_q} \mathcal{D}(G_q, v_i, v_j) \end{equation}

Canvas Method is a method for inference attacks on object detection models. It draws a predicted bounding box distribution on an empty canvas for an attack model input. The canvas is initially set to an image of 300$\times$300 pixels in size, where every pixel has a value of zero and the boxes drawn on the canvas have the same center as the predicted boxes and the same intensity as the prediction scores.

A novel low-resource intrinsic metric to evaluate word embedding quality based on graph modularity.

The core ingredient of CayleyNet is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. The model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Description adapted from: CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

CentripetalNet is a keypoint-based detector which uses centripetal shift to pair corner keypoints from the same instance. CentripetalNet predicts the position and the centripetal shift of the corner points and matches corners whose shifted results are aligned.

Child-Tuning is a fine-tuning technique that updates a subset of parameters (called child network) of large pretrained models via strategically masking out the gradients of the non-child network during the backward process. It decreases the hypothesis space of the model via a task-specific mask applied to the full gradients, helping to effectively adapt the large-scale pretrained model to various tasks and meanwhile aiming to maintain its original generalization ability.

CPT, or Chinese Pre-trained Unbalanced Transformer, is a pre-trained unbalanced Transformer for Chinese natural language understanding (NLU) and natural language generation (NLG) tasks. CPT consists of three parts: a shared encoder, an understanding decoder, and a generation decoder. Two specific decoders with a shared encoder are pre-trained with masked language modeling (MLM) and denoising auto-encoding (DAE) tasks, respectively. With the partially shared architecture and multi-task pre-training, CPT can (1) learn specific knowledge of both NLU or NLG tasks with two decoders and (2) be fine-tuned flexibly that fully exploits the potential of the model. Two specific decoders with a shared encoder are pre-trained with masked language modeling (MLM) and denoising auto-encoding (DAE) tasks, respectively. With the partially shared architecture and multi-task pre-training, CPT can (1) learn specific knowledge of both NLU or NLG tasks with two decoders and (2) be fine-tuned flexibly that fully exploits the potential of the model.

ClassSR is a framework to accelerate super-resolution (SR) networks on large images (2K-8K). ClassSR combines classification and SR in a unified framework. In particular, it first uses a Class-Module to classify the sub-images into different classes according to restoration difficulties, then applies an SR-Module to perform SR for different classes. The Class-Module is a conventional classification network, while the SR-Module is a network container that consists of the to-be-accelerated SR network and its simplified versions.

ClipBERT is a framework for end-to-end-learning for video-and-language tasks, by employing sparse sampling, where only a single or a few sparsely sampled short clips from a video are used at each training step. Two aspects distinguish ClipBERT from previous work. First, in contrast to densely extracting video features (adopted by most existing methods), CLIPBERT sparsely samples only one single or a few short clips from the full-length videos at each training step. The hypothesis is that visual features from sparse clips already capture key visual and semantic information in the video, as consecutive clips usually contain similar semantics from a continuous scene. Thus, a handful of clips are sufficient for training, instead of using the full video. Then, predictions from multiple densely-sampled clips are aggregated to obtain the final video-level prediction during inference, which is less computational demanding. The second differentiating aspect concerns the initialization of model weights (i.e., transfer through pre-training). The authors use 2D architectures (e.g., ResNet-50) instead of 3D features as the visual backbone for video encoding, allowing them to harness the power of image-text pretraining for video-text understanding along with the advantages of low memory cost and runtime efficiency.

Co-Correcting is a noise-tolerant deep learning framework for medical image classification based on mutual learning and annotation correction. It consists of three modules: the dual-network architecture, the curriculum learning module, and the label correction module.

Contrastive BERT is a reinforcement learning agent that combines a new contrastive loss and a hybrid LSTM-transformer architecture to tackle the challenge of improving data efficiency for RL. It uses bidirectional masked prediction in combination with a generalization of recent contrastive methods to learn better representations for transformers in RL, without the need of hand engineered data augmentations. For the architecture, a residual network is used to encode observations into embeddings $Y\_{t}$. $Y_{t}$ is fed through a causally masked GTrXL transformer, which computes the predicted masked inputs $X\_{t}$ and passes those together with $Y\_{t}$ to a learnt gate. The output of the gate is passed through a single LSTM layer to produce the values that we use for computing the RL loss. A contrastive loss is computed using predicted masked inputs $X_{t}$ and $Y_{t}$ as targets. For this, we do not use the causal mask of the Transformer.