This paper models object detection as a regression problem for bounding
boxes and object class probabilities with a single pass through the CNN. The
main contribution is the idea of dividing the image into a 7x7 grid, and having
each cell predict a distribution over class labels as well as a bounding box
for the object whose center falls into it. It's much faster than R-CNN and
Fast R-CNN, as the additional step of extracting region proposals has been
removed.

## Strengths

- Works real-time. Base model runs at 45fps and a faster version goes up to
150fps, and they claim that it's more than twice as fast as other works on
real-time detection.

- End-to-end model; Localization and classification errors can be jointly
optimized.

- YOLO makes more localization errors and fewer background mistakes than
Fast R-CNN, so using YOLO to eliminate false background detections from
Fast R-CNN results in ~3% mAP gain (without much computational time as R-CNN
is much slower).

## Weaknesses / Notes

- Results fall short of state-of-the-art: 57.9% v/s 70.4% mAP (Faster R-CNN).

- Performs worse at detecting small objects, as at most one object per grid
cell can be detected.

An attention-based architecture for combining information from different convolutional layers. The attention values are calculated using an iterative process, making use of a custom squashing function. The evaluations on MNIST show robustness to affine transformations.

# Object detection system overview.

https://i.imgur.com/vd2YUy3.png
 
1.	takes an input image,
2.	extracts around 2000 bottom-up region proposals, 
3.	computes features for each proposal using a large convolutional neural network (CNN), and then
4.	classifies each region using class-specific linear SVMs.
* R-CNN achieves a mean average precision (mAP) of 53.7% on PASCAL VOC 2010.
* On the 200-class ILSVRC2013 detection dataset, R-CNN’s mAP is 31.4%, a large improvement over OverFeat , which had the previous best result at 24.3%.

## There is a 2 challenges faced in object detection 
1.	localization problem
2.	labeling the data

1 localization problem :
*  One approach frames localization as a regression problem.  they report a mAP of 30.5% on VOC 2007 compared to the 58.5% achieved by our method.
* An alternative is to build a sliding-window detector.  considered adopting a sliding-window approach increases the number of convolutional layers to 5, have very large receptive fields (195 x 195 pixels) and strides (32x32 pixels) in the input image, which makes precise localization within the sliding-window paradigm.

2 labeling the data:
* The conventional solution to this problem is to use unsupervised pre-training, followed by supervise fine-tuning 
* supervised pre-training on a large auxiliary dataset (ILSVRC), followed by domain specific fine-tuning on a small dataset (PASCAL),
* fine-tuning for detection improves mAP performance by 8 percentage points. 
* Stochastic gradient descent via back propagation was used to  effective for training convolutional neural networks (CNNs)
 
##  Object detection with R-CNN
This  system consists of three modules
* The first generates category-independent region proposals. These proposals define the set of candidate detections available to our detector.
*  The second module is a large convolutional neural network that extracts a fixed-length feature vector from each region.
* The third module is a set of class specific linear SVMs.

Module design

1 Region proposals 
* which detect mitotic cells by applying a CNN to regularly-spaced square crops.
* use selective search method in fast mode (Capture All Scales, Diversification, Fast to Compute).
* the time spent computing region proposals and features (13s/image on a GPU or 53s/image on a CPU)

2 Feature extraction.
* extract a 4096-dimensional feature vector from each region proposal using the Caffe  implementation of the CNN 
* Features are computed by forward propagating a mean-subtracted 227x227 RGB image through five convolutional layers and two fully connected layers.
* warp all pixels in a tight bounding box around it to the required size
* The feature matrix is typically 2000x4096 

3 Test time detection
* At test time, run selective search on the test image to extract around 2000 region proposals (we use selective search’s “fast mode” in all experiments).
*  warp each proposal and forward propagate it through the CNN in order to compute features. Then, for each class, we score each extracted feature vector using the SVM trained for that class.
* Given all scored regions in an image, we apply a greedy non-maximum suppression (for each class independently) that rejects a region if it has an intersection-over union (IoU) overlap with a higher scoring selected region larger than a learned threshold.
## Training

1 Supervised pre-training:
 * pre-trained the CNN on a large auxiliary dataset (ILSVRC2012 classification)  using image-level annotations only (bounding box labels are not available for this data)

2  Domain-specific fine-tuning.
* use the stochastic gradient descent (SGD) training of the CNN parameters using only warped region proposals with  learning rate of 0.001.

3  Object category classifiers.
*   use intersection-over union (IoU) overlap threshold method  to label a region with The overlap threshold of 0.3.
* Once features are extracted and training labels are applied, we optimize one linear SVM per class.
* adopt the standard hard negative mining method to fit large training data in memory.

### Results on PASCAL VOC 201012

1  VOC 2010
* compared against four strong baselines including SegDPM, DPM, UVA, Regionlets. 
* Achieve a large improvement in mAP, from 35.1% to 53.7% mAP,  while also being much faster
 https://i.imgur.com/0dGX9b7.png
2 ILSVRC2013 detection.
* ran R-CNN on the 200-class ILSVRC2013 detection dataset
* R-CNN achieves a mAP of 31.4%
https://i.imgur.com/GFbULx3.png
#### Performance layer-by-layer, without fine-tuning
1 pool5 layer 
* which is the max pooled output of the network’s fifth and final convolutional layer.
*The pool5 feature map is 6 x6 x 256 = 9216 dimensional
* each pool5 unit has a receptive field of 195x195 pixels in the original 227x227 pixel input

2 Layer fc6
* fully connected to pool5
*  it multiplies a 4096x9216 weight matrix by the pool5 feature map (reshaped as a 9216-dimensional vector) and then adds a vector of biases

3 Layer fc7
* It is implemented by multiplying the features computed by fc6 by a 4096 x 4096 weight matrix, and similarly adding a vector of biases and applying half-wave rectification
#### Performance layer-by-layer, with fine-tuning
* CNN’s parameters  fine-tuned on PASCAL.
* fine-tuning increases mAP by 8.0 % points to 54.2%

### Network architectures
* 16-layer deep network, consisting of 13 layers of 3 _ 3 convolution kernels, with five max pooling layers interspersed, and topped with three fully-connected layers. We refer to this network as “O-Net” for OxfordNet and the baseline as “T-Net” for TorontoNet.
* RCNN with O-Net substantially outperforms R-CNN with TNet, increasing mAP from 58.5% to 66.0%
* drawback in terms of compute time, with in terms of compute time, with than T-Net.

1 The ILSVRC2013 detection dataset
* dataset is split into three sets: train (395,918), val (20,121), and test (40,152) 

#### CNN features for segmentation.
* full R-CNN: The first strategy (full) ignores the re region’s shape and computes CNN features directly on the warped window. Two regions might have very similar bounding boxes while having very little overlap. 
* fg R-CNN: the second strategy (fg) computes CNN features only on a region’s foreground mask. We replace the background with the mean input so that background regions are zero after mean subtraction.
* full+fg R-CNN: The third strategy (full+fg) simply concatenates the full and fg features
 https://i.imgur.com/n1bhmKo.png

Ma et al. detect adversarial examples based on their estimated intrinsic dimensionality. I want to note that this work is also similar to [1] – in both publications, local intrinsic dimensionality is used to analyze adversarial examples. Specifically, the intrinsic dimensionality of a sample is estimated based on the radii $r_i(x)$ of the $k$-nearest neighbors around a sample $x$:

$- \left(\frac{1}{k} \sum_{i = 1}^k \log \frac{r_i(x)}{r_k(x)}\right)^{-1}$.

For details regarding the original, theoretical formulation of local intrinsic dimensionality I refer to the paper. In experiments, the authors show that adversarial examples exhibit a significant higher intrinsic dimensionality than training samples or randomly perturbed examples. This observation allows detection of adversarial examples. A proper interpretation of this finding is, however, missing. It would be interesting to investigate what this finding implies about the properties of adversarial examples.

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.