기법

Residual Normal Distributions are used to help the optimization of VAEs, preventing optimization from entering an unstable region. This can happen due to sharp gradients caused in situations where the encoder and decoder produce distributions far away from each other. The residual distribution parameterizes $q\left(\mathbf{z}|\mathbf{x}\right)$ relative to $p\left(\mathbf{z}\right)$. Let $p\left(z^{i}\_{l}|\mathbf{z}\_{<l}\right) := N \left(\mu\_{i}\left(\mathbf{z}\_{<l}\right), \sigma\_{i}\left(\mathbf{z}\_{<l}\right)\right)$ be a Normal distribution for the $i$th variable in $\mathbf{z}\_{l}$ in prior. Define $q\left(z^{i}\_{l}|\mathbf{z}\_{<l}, x\right) := N\left(\mu\_{i}\left(\mathbf{z}\_{<l}\right) + \Delta\mu\_{i}\left(\mathbf{z}\_{<l}, x\right), \sigma\_{i}\left(\mathbf{z}\_{<l}\right) \cdot \Delta\sigma\_{i}\left(\mathbf{z}\_{<l}, x\right) \right)$, where $\Delta\mu\_{i}\left(\mathbf{z}\_{<l}, \mathbf{x}\right)$ and $\Delta\sigma\_{i}\left(\mathbf{z}\_{<l}, \mathbf{x}\right)$ are the relative location and scale of the approximate posterior with respect to the prior. With this parameterization, when the prior moves, the approximate posterior moves accordingly, if not changed.

Dynamic Sparse Training method based on using the gradient norm to select which weights to remove/add in each mask update.

SNIPER is a multi-scale training approach for instance-level recognition tasks like object detection and instance-level segmentation. Instead of processing all pixels in an image pyramid, SNIPER selectively processes context regions around the ground-truth objects (a.k.a chips). This can help to speed up multi-scale training as it operates on low-resolution chips. Due to its memory-efficient design, SNIPER can benefit from Batch Normalization during training and it makes larger batch-sizes possible for instance-level recognition tasks on a single GPU.

Spatial pooling usually operates on a small region which limits its capability to capture long-range dependencies and focus on distant regions. To overcome this, Hou et al. proposed strip pooling, a novel pooling method capable of encoding long-range context in either horizontal or vertical spatial domains. Strip pooling has two branches for horizontal and vertical strip pooling. The horizontal strip pooling part first pools the input feature $F \in \mathcal{R}^{C \times H \times W}$ in the horizontal direction: \begin{align} y^1 = \text{GAP}^w (X) \end{align} Then a 1D convolution with kernel size 3 is applied in $y$ to capture the relationship between different rows and channels. This is repeated $W$ times to make the output $y_v$ consistent with the input shape: \begin{align} y_h = \text{Expand}(\text{Conv1D}(y^1)) \end{align} Vertical strip pooling is performed in a similar way. Finally, the outputs of the two branches are fused using element-wise summation to produce the attention map: \begin{align} s &= \sigma(Conv^{1\times 1}(y_{v} + y_{h})) \end{align} \begin{align} Y &= s X \end{align} The strip pooling module (SPM) is further developed in the mixed pooling module (MPM). Both consider spatial and channel relationships to overcome the locality of convolutional neural networks. SPNet achieves state-of-the-art results for several complex semantic segmentation benchmarks.

A wavelet scattering transform computes a translation invariant representation, which is stable to deformation, using a deep convolution network architecture. It computes non-linear invariants with modulus and averaging pooling functions. It helps to eliminate the image variability due to translation and is stable to deformations. Image source: Bruna and Mallat

SpineNet is a convolutional neural network backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.

Lightweight or mobile neural networks used for real-time computer vision tasks contain fewer parameters than normal networks, which lead to a constrained performance. In this work, we proposed a novel activation function named Tanh Exponential Activation Function (TanhExp) which can improve the performance for these networks on image classification task significantly. The definition of TanhExp is $f(x) = x tanh(e^x)$. We demonstrate the simplicity, efficiency, and robustness of TanhExp on various datasets and network models and TanhExp outperforms its counterparts in both convergence speed and accuracy. Its behaviour also remains stable even with noise added and dataset altered. We show that without increasing the size of the network, the capacity of lightweight neural networks can be enhanced by TanhExp with only a few training epochs and no extra parameters added.

A method that randomly mask out all features coming from a specific time-step in time-series data. If the model used is independent of uneven sequences or missing data in time-series, like attention-based transformers, the masked time-steps can be just ignored in the forward prediction of the model. Otherwise, they have to be masked out with some numerical value.

Temporal Jittering is a method used in deep learning for video, where we sample multiple training clips from each video with random start times during at every epoch.

TextGrad is a powerful framework building automatic ``differentiation'' via text. TextGrad implements backpropagation through text feedback provided by LLMs, strongly building on the gradient metaphor

VL-T5 is a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation. The model learns to generate labels in text based on the visual and textual inputs. In contrast to other existing methods, the framework unifies tasks as generating text labels conditioned on multimodal inputs. This allows the model to tackle vision-and-language tasks with unified text generation objective. The models use text prefixes to adapt to different tasks.

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.

Kernel density matrices provide a simpler yet effective mechanism for representing joint probability distributions of both continuous and discrete random variables. This abstraction allows the construction of differentiable models for density estimation, inference, and sampling, and enables their integration into end-to-end deep neural models.

Accordion is a gradient communication scheduling algorithm that is generic across models while imposing low computational overheads. Accordion inspects the change in the gradient norms to detect critical regimes and adjusts the communication schedule dynamically. Accordion works for both adjusting the gradient compression rate or the batch size without additional parameter tuning.

Please enter a description about the method here

Anti-Alias Downsampling (AA) aims to improve the shift-equivariance of deep networks. Max-pooling is inherently composed of two operations. The first operation is to densely evaluate the max operator and second operation is naive subsampling. AA is proposed as a low-pass filter between them to achieve practical anti-aliasing in any existing strided layer such as strided convolution. The smoothing factor can be adjusted by changing the blur kernel filter size, where a larger filter size results in increased blur.

BASNet, or Boundary-Aware Segmentation Network, is an image segmentation architecture that consists of a predict-refine architecture and a hybrid loss. The proposed BASNet comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation. The predict-refine architecture consists of a densely supervised encoder-decoder network and a residual refinement module, which are respectively used to predict and refine a segmentation probability map. The hybrid loss is a combination of the binary cross entropy, structural similarity and intersection-over-union losses, which guide the network to learn three-level (i.e., pixel-, patch- and map- level) hierarchy representations.

Big-Little Modules are blocks for image models that have two branches: each of which represents a separate block from a deep model and a less deep counterpart. They were proposed as part of the BigLittle-Net architecture. The two branches are fused with a linear combination and unit weights. These two branches are known as Big-Branch (more layers and channels at low resolutions) and Little-Branch (fewer layers and channels at high resolution).

BigBiGAN is a type of BiGAN with a BigGAN image generator. The authors initially used ResNet as a baseline for the encoder $\mathcal{E}$ followed by a 4-layer MLP with skip connections, but they experimented with RevNets and found they outperformed with increased network width, so opted for this type of encoder for the final architecture.

CRF-RNN is a formulation of a CRF as a Recurrent Neural Network. Specifically it formulates mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks.

The composed video retrieval (CoVR) task is a new task, where the goal is to find a video that matches both a query image and a query text. The query image represents a visual concept that the user is interested in, and the query text specifies how the concept should be modified or refined. For example, given an image of a fountain and the text during show at night, the CoVR task is to retrieve a video that shows the fountain at night with a show.

ComplEx model trained with a nuclear norm regularizer

Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems.

Recursive functions with heteroscedasticity, sparse and high-variance target distributions introduces a huge complexity that makes their accurate modeling with Neural Networks a difficult task. A main property of recursive maps (e.g factorial function), is their dramatic growth and drop. Learning this recursive behavior requires not only fitting high-frequency patterns within a bounded region but also successfully extrapolating those patterns beyond that region. In time series prediction tasks, capturing periodic even behavior is a challenge. Various methods have been employed to model periodic patterns effectively. However, these approaches typically deal with uni-modal data that also exhibit relatively low variance in both In-Distribution (ID) and Out-Of-Distribution (OOD) regions and do not generalize well to recursive problems with the high-variance observed in our context. Thus, to enable Transformers to capture such behavior and perform proper inference for multi-modal recursive problems, we enhance them by introducing the Dynamic Range Activator (DRA). The DRA is designed to handle the recursive and factorial growth properties inherent in enumerative problems with minimal computational overhead and can be integrated into existing neural networks without requiring significant architectural changes. DRA integrates both harmonic and hyperbolic components as follows, \begin{equation} \mathrm{DRA}(x) := x + a \sin^2\left(\frac{x}{b}\right) + c \cos(bx) + d \tanh(bx) ,, \end{equation} where $a, b, c, d$ are learnable parameters. It allows the function to simultaneously model periodic data (through sine and cosine) and rapid growth or attenuation (through the hyperbolic tangent) response.

Decorrelated Batch Normalization (DBN) is a normalization technique which not just centers and scales activations but whitens them. ZCA whitening instead of PCA whitening is employed since PCA whitening causes a problem called stochastic axis swapping, which is detrimental to learning.

DeepLabv2 is an architecture for semantic segmentation that build on DeepLab with an atrous spatial pyramid pooling scheme. Here we have parallel dilated convolutions with different rates applied in the input feature map, which are then fused together. As objects of the same class can have different sizes in the image, ASPP helps to account for different object sizes.

Deformable RoI Pooling adds an offset to each bin position in the regular bin partition of the RoI Pooling. Similarly, the offsets are learned from the preceding feature maps and the RoIs, enabling adaptive part localization for objects with different shapes.

Distributional Generalization is a type of generalization that roughly states that outputs of a classifier at train and test time are close as distributions, as opposed to close in just their average error. This behavior is not captured by classical generalization, which would only consider the average error and not the distribution of errors over the input domain.

DouZero is an AI system for the card game DouDizhu that enhances traditional Monte-Carlo methods with deep neural networks, action encoding, and parallel actors. The Q-network of DouZero consists of an LSTM to encode historical actions and six layers of MLP with hidden dimension of 512. The network predicts a value for a given state-action pair based on the concatenated representation of action and state.

Traditional methods are based on block-wise regression. This framework, Enhanced Blockwise Classification (EBC), however, is based on the idea that aims to classify the count value within each block into several pre-defined bins. The enhancement comes from 3 aspects: discretization policy, label correction and loss function. Notice that the original block-wise classification concept was introduced by Liu et al. in Counting Objects by Blockwise Classification.