**XGrad-CAM**, or **Axiom-based Grad-CAM**, is a class-discriminative visualization method and able to highlight the regions belonging to the objects of interest. Two axiomatic properties are introduced in the derivation of XGrad-CAM: Sensitivity and Conservation. In particular, the proposed XGrad-CAM is still a linear combination of feature maps, but able to meet the constraints of those two axioms.

**Stein Variational Policy Gradient**, or **SVPG**, is a policy gradient based method in reinforcement learning that uses Stein Variational Gradient Descent to allow simultaneous exploitation and exploration of multiple policies. Unlike traditional policy optimization which attempts to learn a single policy, SVPG models a distribution of policy parameters, where samples from this distribution will represent strong policies.  SVPG optimizes this distribution of policy parameters with (relative) [entropy regularization](https://paperswithcode.com/method/entropy-regularization). The (relative) entropy term explicitly encourages exploration in the parameter space while also optimizing the expected utility of polices drawn from this distribution. Stein variational gradient descent (SVGD) is then used to optimize this distribution. SVGD leverages efficient deterministic dynamics to transport a set of particles to approximate given target posterior distributions. 

The update takes the form:

$$ $$

$$ \nabla\theta\_i = \frac{1} {n}\sum\_{j=1}^n \nabla\_{\theta\_{j}} \left(\frac{1}{\alpha} J(\theta\_{j}) + \log q\_0(\theta\_j)\right)k(\theta\_j, \theta\_i) + \nabla\_{\theta\_j} k(\theta\_j, \theta\_i)$$

Note that here the magnitude of $\alpha$ adjusts the relative importance between the policy gradient and the prior term $\nabla_{\theta_j} \left(\frac{1}{\alpha} J(\theta_j) + \log q_0(\theta_j)\right)k(\theta_j, \theta_i)$ and the repulsive term $\nabla_{\theta_j} k(\theta_j, \theta_i)$. The repulsive functional is used to diversify particles to enable parameter exploration. A suitable $\alpha$ provides a good trade-off between exploitation and exploration. If $\alpha$ is too large, the Stein gradient would only drive the particles to be consistent with the prior $q_0$. As $\alpha \to 0$, this algorithm is reduced to running $n$ copies of independent policy gradient algorithms, if $\{\theta_i\}$ are initialized very differently. A careful annealing scheme of $\alpha$ allows efficient exploration in the beginning of training and later focuses on exploitation towards the end of training.

SVPG

Stein Variational Policy Gradient

XGrad-CAM

Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

**EfficientNetV2** is a type convolutional neural network that has faster training speed and better parameter efficiency than [previous models](https://paperswithcode.com/method/efficientnet). To develop these models, the authors use a combination of training-aware [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) and scaling, to jointly optimize training speed. The models were searched from the search space enriched with new ops such as [Fused-MBConv](https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html).

Architecturally the main differences are:

- EfficientNetV2 extensively uses both [MBConv](https://paperswithcode.com/method/inverted-residual-block)  and the newly added fused-MBConv in the early layers.
- EfficientNetV2 prefers smaller expansion ratio for [MBConv](https://paperswithcode.com/method/inverted-residual-block) since smaller expansion ratios tend to have less memory access overhead.
- EfficientNetV2 prefers smaller 3x3 kernel sizes, but it adds more layers to compensate the reduced receptive field resulted from the smaller kernel size. 
- EfficientNetV2 completely removes the last stride-1 stage in the original EfficientNet, wperhaps due to its large parameter size and memory access overhead.

Source	Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com