**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 $0$.

**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)

SEER

Self-supervised Pretraining of Visual Features in the Wild

Varifocal Loss

VarifocalNet: An IoU-aware Dense Object Detector

**lda2vec** builds representations over both words and documents by mixing word2vec’s skipgram architecture with Dirichlet-optimized sparse topic mixtures. 

The Skipgram Negative-Sampling (SGNS) objective of word2vec is modified to utilize document-wide feature vectors while simultaneously learning continuous document weights loading onto topic vectors. The total loss term $L$ is the sum of the Skipgram Negative Sampling Loss (SGNS) $L^{neg}\_{ij}$ with the addition of a Dirichlet-likelihood term over document weights, $L\_{d}$. The loss is conducted using a context vector, $\overrightarrow{c\_{j}}$ , pivot word vector $\overrightarrow{w\_{j}}$, target word vector $\overrightarrow{w\_{i}}$, and negatively-sampled word vector $\overrightarrow{w\_{l}}$:

$$ L = L^{d} + \Sigma\_{ij}L^{neg}\_{ij} $$

$$L^{neg}\_{ij} = \log\sigma\left(c\_{j}\cdot\overrightarrow{w\_{i}}\right) + \sum^{n}\_{l=0}\sigma\left(-\overrightarrow{c\_{j}}\cdot\overrightarrow{w\_{l}}\right)$$

Source	VarifocalNet: An IoU-aware Dense Object Detector
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com