**Dilated Bottleneck Block** is an image model block used in the [DetNet](https://paperswithcode.com/method/detnet) convolutional neural network architecture. It employs a bottleneck structure with dilated convolutions to efficiently enlarge the receptive field.

Dilated Bottleneck Block

DetNet: A Backbone network for Object Detection

A **BiFPN**, or **Weighted Bi-directional Feature Pyramid Network**, is a type of feature pyramid network which allows easy and fast multi-scale feature fusion. It incorporates the multi-level feature fusion idea from [FPN](https://paperswithcode.com/method/fpn), [PANet](https://paperswithcode.com/method/panet) and [NAS-FPN](https://paperswithcode.com/method/nas-fpn) that enables information to flow in both the top-down and bottom-up directions, while using regular and efficient connections. It also utilizes a fast normalized fusion technique. Traditional approaches usually treat all features input to the FPN equally, even those with different resolutions. However, input features at different resolutions often have unequal contributions to the output features. Thus, the BiFPN adds an additional weight for each input feature allowing the network to learn the importance of each. All regular convolutions are also replaced with less expensive depthwise separable convolutions.

Comparing with PANet, PANet added an extra bottom-up path for information flow at the expense of more computational cost. Whereas BiFPN optimizes these cross-scale connections by removing nodes with a single input edge, adding an extra edge from the original input to output node if they are on the same level, and treating each bidirectional path as one feature network layer (repeating it several times for more high-level future fusion).

BiFPN

EfficientDet: Scalable and Efficient Object Detection

A **Masked Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) which masks certain pixels so that the model can only predict based on pixels already seen. This type of convolution was introduced with [PixelRNN](https://paperswithcode.com/method/pixelrnn) generative models, where an image is generated pixel by pixel, to ensure that the model was conditional only on pixels already visited.

Masked Convolution

Pixel Recurrent Neural Networks

The principle of parsimony, also known as Occam's razor, elucidates the preference for the simplest explanation that provides optimal results when faced with multiple options. Thus, we can assert that the principle of parsimony is justified by "the assumption that is both the simplest and contains all the necessary information required to comprehend the experiment at hand." This principle finds application in various scenarios or events in our daily lives, including predictions in Data Science models.

It is widely recognized that a less complex model will produce more stable predictions, exhibit greater resilience to noise and disturbances, and be more manageable for maintenance and analysis. Additionally, reducing the number of features can lead to further cost savings by diminishing the use of sensors, lowering energy consumption, minimizing information acquisition costs, reducing maintenance requirements, and mitigating the necessity to retrain models due to feature fluctuations caused by noise, outliers, data drift, etc.

The concurrent optimization of hyperparameters (HO) and feature selection (FS) for achieving Parsimonious Model Selection (PMS) is an ongoing area of active research. Nonetheless, the effective selection of appropriate hyperparameters and feature subsets presents a challenging combinatorial problem, frequently requiring the application of efficient heuristic methods.

AutoParsimony

**SHAP**, or **SHapley Additive exPlanations**, is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. Shapley values are approximating using Kernel SHAP, which uses a weighting kernel for the approximation, and DeepSHAP, which uses DeepLift to approximate them.

SHAP

A Unified Approach to Interpreting Model Predictions

**Self-adaptive Training** is a training algorithm that dynamically corrects problematic training labels by model predictions to improve generalization of deep learning for potentially corrupted training data. Accumulated predictions are used to augment the training dynamics. The use of an exponential-moving-average scheme alleviates the instability issue of model predictions, smooths out the training target during the training process and enables the algorithm to completely change the training labels if necessary.

Self-adaptive Training

Self-Adaptive Training: beyond Empirical Risk Minimization

**Position-Sensitive RoI Pooling layer** aggregates the outputs of the last convolutional layer and generates scores for each RoI. Unlike [RoI Pooling](https://paperswithcode.com/method/roi-pooling), PS RoI Pooling conducts selective pooling, and each of the $k$ × $k$ bin aggregates responses from only one score map out of the bank of $k$ × $k$ score maps. With end-to-end training, this RoI layer shepherds the last convolutional layer to learn specialized position-sensitive score maps.

Position-Sensitive RoI Pooling

R-FCN: Object Detection via Region-based Fully Convolutional Networks

The **Vision Transformer**, or **ViT**, is a model for image classification that employs a [Transformer](https://paperswithcode.com/method/transformer)-like architecture over patches of the image.  An image is split into fixed-size patches, each of them are then linearly embedded, position embeddings are added, and the resulting sequence of vectors is fed to a standard [Transformer](https://paperswithcode.com/method/transformer) encoder. In order to perform classification, the standard approach of adding an extra learnable “classification token” to the sequence is used.

Vision Transformer

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

**BAGUA** is a communication framework whose design goal is to provide a system abstraction that is both flexible and modular to support state-of-the-art system relaxation techniques of distributed training. The abstraction goes beyond parameter server and Allreduce paradigms, and provides a collection of MPI-style collective operations to facilitate communications with different precision and centralization strategies.

BAGUA

BAGUA: Scaling up Distributed Learning with System Relaxations

DGCNN involves neural networks that read the graphs directly and learn a classification function. There are two main challenges: 1) how to extract useful features characterizing the rich information encoded in a graph for classification purpose, and 2) how to sequentially read a graph in a meaningful and consistent order. To address the first challenge, we design a localized graph convolution model and show its connection with two graph kernels. To address the second challenge, we design a novel SortPooling layer which sorts graph vertices in a consistent order so that traditional neural networks can be trained on the graphs.

Description and image from: [An End-to-End Deep Learning Architecture for Graph Classification](https://muhanzhang.github.io/papers/AAAI_2018_DGCNN.pdf)

DGCNN

An End-to-End Deep Learning Architecture for Graph Classification

Deep-learning models estimate values using backpropagation. The activation function within hidden layers is a critical component to minimizing loss in deep neural-networks. Rectified Linear (ReLU) has been the dominant activation function for the past decade. Swish and Mish are newer activation functions that have shown to yield better results than ReLU given specific circumstances. Phish is a novel activation function proposed here. It is a composite function defined as f(x) = xTanH(GELU(x)), where no discontinuities are apparent in the differentiated graph on the domain observed. Generalized networks were constructed using different activation functions. SoftMax was the output function. Using images from MNIST and CIFAR-10 databanks, these networks were trained to minimize sparse categorical crossentropy. A large scale cross-validation was simulated using stochastic Markov chains to account for the law of large numbers for the probability values. Statistical tests support the research hypothesis stating Phish could outperform other activation functions in classification. Future experiments would involve testing Phish in unsupervised learning algorithms and comparing it to more activation functions.

Phish

Phish: A Novel Hyper-Optimizable Activation Function

**Local Importance-based Pooling (LIP)** is a pooling layer that can enhance discriminative features during the downsampling procedure by learning adaptive importance weights based on inputs. By using a learnable network $G$ in $F$, the importance function now is not limited in hand-crafted forms and able to learn the criterion for the discriminativeness of features. Also, the window size of LIP is restricted to be not less than stride to fully utilize the feature map and avoid the issue of fixed interval sampling scheme. More specifically, the importance function in LIP is implemented by a tiny fully convolutional network, which learns to produce the importance map based on inputs in an end-to-end manner.

Local Importance-based Pooling

LIP: Local Importance-based Pooling

**MUSIQ**, or **Multi-scale Image Quality Transformer**, is a [Transformer](https://paperswithcode.com/method/transformer)-based model for multi-scale image quality assessment. It processes native resolution images with varying sizes and aspect ratios. In MUSIQ, we construct a multi-scale image representation as input, including the native resolution image and its ARP resized variants.  Each image is split into fixed-size patches which are embedded by a patch encoding module (blue boxes). To capture 2D structure of the image and handle images of varying aspect ratios, the spatial embedding is encoded by hashing the patch position $(i,j)$ to $(t_{i},t_{j})$ within a grid of learnable embeddings (red boxes). Scale Embedding (green boxes) is introduced to capture scale information. The Transformer encoder takes the input tokens and performs multi-head self-attention. To predict the image quality, MUSIQ follows a common strategy in Transformers to add an [CLS] token to the sequence to represent the whole multi-scale input and the corresponding Transformer output is used as the final representation.

MUSIQ

MUSIQ: Multi-scale Image Quality Transformer

**Darknet-19** is a convolutional neural network that is used as the backbone of [YOLOv2](https://paperswithcode.com/method/yolov2).  Similar to the [VGG](https://paperswithcode.com/method/vgg) models it mostly uses $3 \times 3$ filters and doubles the number of channels after every pooling step. Following the work on Network in Network (NIN) it uses [global average pooling](https://paperswithcode.com/method/global-average-pooling) to make predictions as well as $1 \times 1$ filters to compress the feature representation between $3 \times 3$ convolutions. [Batch Normalization](https://paperswithcode.com/method/batch-normalization) is used to stabilize training, speed up convergence, and regularize the model batch.

Darknet-19

YOLO9000: Better, Faster, Stronger

**Fraternal Dropout** is a regularization method for recurrent neural networks that trains two identical copies of an RNN (that share parameters) with different [dropout](https://paperswithcode.com/method/dropout) masks while minimizing the difference between their (pre-[softmax](https://paperswithcode.com/method/softmax)) predictions. This encourages the representations of RNNs to be invariant to dropout mask, thus being robust.

Fraternal Dropout

R-Mix (Random Mix-up) is a Mix-up family Data Augmentation method. It combines random Mix-up with Saliency-guided mix-up, producing a procedure that is fast and performant, while reserving good characteristics of Saliency-guided Mix-up such as low Expected Calibration Error and high Weakly-supervised Object Localization accuracy.

R-Mix

Expeditious Saliency-guided Mix-up through Random Gradient Thresholding

**NT-ASGD**, or **Non-monotonically Triggered ASGD**, is an averaged stochastic gradient descent technique. 

In regular ASGD, we take steps identical to [regular SGD](https://paperswithcode.com/method/sgd) but instead of returning the last iterate as the solution, we return $\frac{1}{\left(K-T+1\right)}\sum^{T}\_{i=T}w\_{i}$, where $K$ is the total number of iterations and $T < K$ is a user-specified averaging trigger.

NT-ASGD has a non-monotonic criterion that conservatively triggers the averaging when the validation metric fails to improve for multiple cycles. Given that the choice of triggering is irreversible, this conservatism ensures that the randomness of training does not play a major role in the decision.

NT-ASGD

Regularizing and Optimizing LSTM Language Models

**Guided Anchoring** is an anchoring scheme for object detection which leverages semantic features to guide the anchoring. The method is motivated by the observation that objects are not distributed evenly over the image. The scale of an object is also closely related to the imagery content, its location and geometry of the scene. Following this intuition, the method generates sparse anchors in two steps: first identifying sub-regions that may contain objects and then determining the shapes at different locations.

AI Glossary

Dilated Bottleneck Block

BiFPN

Masked Convolution

Automatic Search for Parsimonious Models

Shapley Additive Explanations

Self-adaptive Training

Position-Sensitive RoI Pooling

Vision Transformer

BAGUA

Deep Graph Convolutional Neural Network

Phish: A Novel Hyper-Optimizable Activation Function

Local Importance-based Pooling

MUSIQ

Darknet-19

Fraternal Dropout

Random Mix-up

Non-monotonically Triggered ASGD

Guided Anchoring