Please enter a description about the method here

A **Fractal Block** is an image model block that utilizes an expansion rule that yields a structural layout of truncated fractals. For the base case where $f\_{1}\left(z\right) = \text{conv}\left(z\right)$ is a convolutional layer, we then have recursive fractals of the form:

$$ f\_{C+1}\left(z\right) = \left[\left(f\_{C}\circ{f\_{C}}\right)\left(z\right)\right] \oplus \left[\text{conv}\left(z\right)\right]$$

Where $C$ is the number of columns. For the join layer (green in Figure), we use the element-wise mean rather than concatenation or addition.

Fractal Block

FractalNet: Ultra-Deep Neural Networks without Residuals

Low Rank Tensor Learning Paradigms

**SimCLRv2** is a semi-supervised learning method for learning from few labeled examples while making best use of a large amount of unlabeled data. It is a modification of a recently proposed contrastive learning framework, [SimCLR](https://www.paperswithcode.com/method/simclr). It improves upon it in three major ways:

1. To fully leverage the power of general pre-training, larger [ResNet](https://paperswithcode.com/method/resnet) models are explored. Unlike SimCLR and other previous work, whose largest model is ResNet-50 (4×), SimCLRv2 trains models that are deeper but less wide. The largest model trained is a 152 layer ResNet with 3× wider channels and [selective kernels](https://paperswithcode.com/method/selective-kernel-convolution) (SK), a channel-wise attention mechanism that improves the parameter efficiency of the network. By scaling up the model from ResNet-50 to ResNet-152 (3×+SK), a 29% relative improvement is obtained in top-1 accuracy when fine-tuned on 1% of labeled examples.

2. The capacity of the non-linear network $g(·)$ (a.k.a. projection head) is increased, by making it deeper. Furthermore, instead of throwing away $g(·)$ entirely after pre-training as in SimCLR, fine-tuning occurs from a middle layer. This small change yields a significant improvement for both linear evaluation and fine-tuning with only a few labeled examples. Compared to SimCLR with 2-layer projection head, by using a 3-layer projection head and fine-tuning from the 1st layer of projection head, it results in as much as 14% relative improvement in top-1 accuracy when fine-tuned on 1% of labeled examples.

3. The memory mechanism of [MoCo v2](https://paperswithcode.com/method/moco-v2) is incorporated, which designates a memory network (with a moving average of weights for stabilization) whose output will be buffered as negative examples. Since training is based on large mini-batch which already supplies many contrasting negative examples, this change yields an improvement of ∼1% for linear evaluation as well as when fine-tuning on 1% of labeled examples.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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