Image Classification

A CNN-based network that uses the ANDHRA module to split the network into multiple parallel branches, leading to multiple network outputs. For a network with three ANDHRA modules with a branching factor of two, the total output heads are 2ˆ3 = 8.

The Adaptive Parametric activation (APA) is defined as: $APA(z,λ,κ) = (λ exp(−κz) + 1) ^{\frac{1}{−λ}}$, where $λ$ and $κ$ are learnable parameters. This activation function is a generalisation of the Sigmoid and the Gumbel activation functions and it is expressive and versatile. For example, APA can be used inside the channel attention mechanism instead of the Sigmoid activation, or it can be used inside the intermediate layers using the Adaptive Generalised Linear Unit (AGLU): $AGLU(z,λ,κ) = z APA(z,λ,κ)$.

ANDHRA module splits the neural network into multiple “branches” (representing different paths) that process the same input signal in parallel, each corresponding to different possible outcomes.

Batch-Channel Normalization, or BCN, uses batch knowledge to prevent channel-normalized models from getting too close to "elimination singularities". Elimination singularities correspond to the points on the training trajectory where neurons become consistently deactivated. They cause degenerate manifolds in the loss landscape which will slow down training and harm model performances.

Batchboost is a variation on MixUp that instead of mixing just two images, mixes many images together.

Blink is a communication library for inter-GPU parameter exchange that achieves near-optimal link utilization. To handle topology heterogeneity from hardware generations or partial allocations from cluster schedulers, Blink dynamically generates optimal communication primitives for a given topology. Blink probes the set of links available for a given job at runtime and builds a topology with appropriate link capacities. Given the topology, Blink achieves the optimal communication rate by packing spanning trees, that can utilize more links (Lovasz, 1976; Edmonds, 1973) when compared to rings. The authors use a multiplicative-weight update based approximation algorithm to quickly compute the maximal packing and extend the algorithm to further minimize the number of trees generated. Blink’s collectives extend across multiple machines effectively utilizing all available network interfaces.

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

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.

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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

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.

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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.

Demon Adam is a stochastic optimizer where the Demon momentum rule is applied to the Adam optimizer. $$ \beta\_{t} = \beta\_{init}\cdot\frac{\left(1-\frac{t}{T}\right)}{\left(1-\beta\_{init}\right) + \beta\_{init}\left(1-\frac{t}{T}\right)} $$ $$ m\_{t, i} = g\_{t, i} + \beta\_{t}m\_{t-1, i} $$ $$ v\_{t+1} = \beta\_{2}v\_{t} + \left(1-\beta\_{2}\right)g^{2}\_{t} $$ $$ \theta_{t} = \theta_{t-1} - \eta\frac{\hat{m}\_{t}}{\sqrt{\hat{v}\_{t}} + \epsilon} $$

Demon CM, or SGD with Momentum and Demon, is the Demon momentum rule applied to SGD with momentum. $$ \beta\_{t} = \beta\_{init}\cdot\frac{\left(1-\frac{t}{T}\right)}{\left(1-\beta\_{init}\right) + \beta\_{init}\left(1-\frac{t}{T}\right)} $$ $$ \theta\_{t+1} = \theta\_{t} - \eta{g}\_{t} + \beta\_{t}v\_{t} $$ $$ v\_{t+1} = \beta\_{t}{v\_{t}} - \eta{g\_{t}} $$

DenseNAS is a neural architecture search method that utilises a densely connected search space. The search space is represented as a dense super network, which is built upon designed routing blocks. In the super network, routing blocks are densely connected and we search for the best path between them to derive the final architecture. A chained cost estimation algorithm is used to approximate the model cost during the search.

DenseNAS-A is a mobile convolutional neural network discovered through the DenseNAS neural architecture search method. The basic building block is MBConvs, or inverted bottleneck residuals, from the MobileNet architectures.

DenseNAS-B is a mobile convolutional neural network discovered through the DenseNAS neural architecture search method. The basic building block is MBConvs, or inverted bottleneck residuals, from the MobileNet architectures.

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.

Differentiable simultaneous optimization of hyperparameters and neural network architecture. Also a Neural Architecture Search (NAS) method.

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The Exponential Linear Squashing Activation Function, or ELiSH, is an activation function used for neural networks. It shares common properties with Swish, being made up of an ELU and a Sigmoid: $$f\left(x\right) = \frac{x}{1+e^{-x}} \text{ if } x \geq 0 $$ $$f\left(x\right) = \frac{e^{x} - 1}{1+e^{-x}} \text{ if } x < 0 $$ The Sigmoid part of ELiSH improves information flow, while the linear parts solve issues of vanishing gradients.

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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