**SEER** is a self-supervised learning approach for training large models on random, uncurated images with no supervision. It trains [RegNet-Y](https://paperswithcode.com/method/regnet-y) architectures with the [SwAV](https://paperswithcode.com/method/swav). Several adjustments are made to self-supervised training to make it work at a larger scale, including using a [cosine learning schedule](https://paperswithcode.com/method/cosine-annealing)

**Disentangled Attribution Curves (DAC)** provide interpretations of tree ensemble methods in the form of (multivariate) feature importance curves. For a given variable, or group of variables, [DAC](https://paperswithcode.com/method/dac) plots the importance of a variable(s) as their value changes.

The Figure to the right shows an example. The tree depicts a decision tree which performs binary classification using two features (representing the XOR function). In this problem, knowing the value of one of the features without knowledge of the other feature yields no information - the classifier still has a 50% chance of predicting either class. As a result, DAC produces curves which assign 0 importance to either feature on its own. Knowing both features yields perfect information about the classifier, and thus the DAC curve for both features together correctly shows that the interaction of the features produces the model’s predictions.

Disentangled Attribution Curves

Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees

SEER

Self-supervised Pretraining of Visual Features in the Wild

**Varifocal Loss** is a loss function for training a dense object detector to predict the IACS, inspired by [focal loss](https://paperswithcode.com/method/focal-loss). Unlike the focal loss that deals with positives and negatives equally, Varifocal Loss treats them asymmetrically.

$$ VFL\left(p, q\right) = −q\left(q\log\left(p\right) + \left(1 − q\right)\log\left(1 − p\right)\right) \text{ if } q > 0 $$

$$ VFL\left(p, q\right) = −\alpha{p^{\gamma}}\log\left(1-p\right) $$

where $p$ is the predicted IACS and $q$ is the target IoU score.

For a positive training example, $q$ is set as the IoU between the generated bounding box and the ground-truth one
(gt IoU), whereas for a negative training example, the training target $q$ for all classes is $0$.

Source	Self-supervised Pretraining of Visual Features in the Wild
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com