Deeper networks should never have a higher **training** error than smaller ones. In the worst case, the layers should "simply" learn identities. It seems as this is not so easy with conventional networks, as they get much worse with more layers. So the idea is to add identity functions which skip some layers. The network only has to learn the **residuals**. 

Advantages:

* Learning the identity becomes learning 0 which is simpler
* Loss in information flow in the forward pass is not a problem anymore
    * No vanishing / exploding gradient
* Identities don't have parameters to be learned

## Evaluation

The learning rate starts at 0.1 and is divided by 10 when the error plateaus. Weight decay of 0.0001 ($10^{-4}$), momentum of 0.9. They use mini-batches of size 128.

* ImageNet ILSVRC 2015: 3.57% (ensemble)
* CIFAR-10: 6.43%
* MS COCO: 59.0% mAp@0.5 (ensemble)
* PASCAL VOC 2007: 85.6% mAp@0.5
* PASCAL VOC 2012: 83.8% mAp@0.5

## See also

* [DenseNets](http://www.shortscience.org/paper?bibtexKey=journals/corr/1608.06993)

Liang et al. propose a perturbation-based approach for detecting out-of-distribution examples using a network’s confidence predictions. In particular, the approaches based on the observation that neural network’s make more confident predictions on images from the original data distribution, in-distribution examples, than on examples taken from a different distribution (i.e., a different dataset), out-distribution examples. This effect can further be amplified by using a temperature-scaled softmax, i.e.,

$ S_i(x, T) = \frac{\exp(f_i(x)/T)}{\sum_{j = 1}^N \exp(f_j(x)/T)}$

where $f_i(x)$ are the predicted logits and $T$ a temperature parameter. Based on these softmax scores, perturbations $\tilde{x}$ are computed using

$\tilde{x} = x - \epsilon \text{sign}(-\nabla_x \log S_{\hat{y}}(x;T))$

where $\hat{y}$ is the predicted label of $x$. This is similar to “one-step” adversarial examples; however, in contrast of minimizing the confidence of the true label, the confidence in the predicted label is maximized. This, applied to in-distribution and out-distribution examples is illustrated in Figure 1 and meant to emphasize the difference in confidence. Afterwards, in- and out-distribution examples can be distinguished using simple thresholding on the predicted confidence, as shown in various experiment, e.g., on Cifar10 and Cifar100.

https://i.imgur.com/OjDVZ0B.png
Figure 1: Illustration of the proposed perturbation to amplify the difference in confidence between in- and out-distribution examples.

Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

In object detection the boost in speed and accuracy is mostly gained through network architecture changes.This paper takes a different route towards achieving that goal,They introduce a new loss function called focal loss.

The authors identify class imbalance as the main obstacle toward one stage detectors achieving results which are as good as two stage detectors.

The loss function they introduce is a dynamically scaled cross entropy loss,Where the scaling factor decays to zero as the confidence in the correct class increases.

They add a modulating factor  as shown in the image below to the cross- entropy loss  https://i.imgur.com/N7R3M9J.png
Which ends up looking like this https://i.imgur.com/kxC8NCB.png
in experiments though they add an additional alpha term to it,because it gives them better results.

**Retina Net**

The network consists of a single unified network which is composed of a backbone network and two task specific subnetworks.The backbone network computes the feature maps for the input images.The first sub-network helps in object classification of the backbone networks output and the second sub-network helps in bounding box regression.
The backbone network they use is Feature Pyramid Network,Which they build on top of ResNet.

Frankle and Carbin discover so-called winning tickets, subset of weights of a neural network that are sufficient to obtain state-of-the-art accuracy. The lottery hypothesis states that dense networks contain subnetworks – the winning tickets – that can reach the same accuracy when trained in isolation, from scratch. The key insight is that these subnetworks seem to have received optimal initialization. Then, given a complex trained network for, e.g., Cifar, weights are pruned based on their absolute value – i.e., weights with small absolute value are pruned first. The remaining network is trained from scratch using the original initialization and reaches competitive performance using less than 10% of the original weights. As soon as the subnetwork is re-initialized, these results cannot be reproduced though. This suggests that these subnetworks obtained some sort of “optimal” initialization for learning.

Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

This paper presents a variational approach to the maximisation of mutual information in the context of a reinforcement learning agent. Mutual information in this context can provide a learning signal to the agent that is "intrinsically motivated", because it relies solely on the agent's state/beliefs and does not require from the ("outside") user an explicit definition of rewards.

Specifically, the learning objective, for a current state s, is the mutual information between the sequence of K actions a proposed by an exploration distribution $w(a|s)$ and the final state s' of the agent after performing these actions. To understand what the properties of this objective, it is useful to consider the form of this mutual information as a difference of conditional entropies:

$$I(a,s'|s) = H(a|s) - H(a|s',s)$$

Where $I(.|.)$ is the (conditional) mutual information and $H(.|.)$ is the (conditional) entropy. This objective thus asks that the agent find an exploration distribution that explores as much as possible (i.e. has high $H(a|s)$ entropy) but is such that these actions have predictable consequences (i.e. lead to predictable state s' so that $H(a|s',s)$ is low). So one could think of the agent as trying to learn to have control of as much of the environment as possible, thus this objective has also been coined as "empowerment".

The main contribution of this work is to show how to train, on a large scale (i.e. larger state space and action space) with this objective, using neural networks. They build on a variational lower bound on the mutual information and then derive from it a stochastic variational training algorithm for it. The procedure has 3 components: the exploration distribution $w(a|s)$, the environment $p(s'|s,a)$ (can be thought as an encoder, but which isn't modeled and is only interacted with/sampled from) and the planning model $p(a|s',s)$ (which is modeled and can be thought of as a decoder). The main technical contribution is in how to update the exploration distribution (see section 4.2.2 for the technical details).

This approach exploits neural networks of various forms. Neural autoregressive generative models are also used as models for the exploration distribution as well as the decoder or planning distribution. Interestingly, the framework allows to also learn the state representation s as a function of some "raw" representation x of states. For raw states corresponding to images (e.g. the pixels of the screen image in a game), CNNs are used.