This paper presents a combination of the inception architecture
with residual networks. This is done by adding a shortcut connection
to each inception module. This can alternatively be seen as a resnet where
the 2 conv layers are replaced by a (slightly modified) inception module.
The paper (claims to) provide results against the hypothesis that adding residual
connections improves training, rather increasing the model size is what makes the difference.

TLDR; The authors use an attention mechanism in image caption generation, allowing the decoder RNN focus on specific parts of the image. In order find the correspondence between words and image patches, the RNN uses a lower convolutional layer as its input (before pooling). The authors propose both a "hard" attention (trained using sampling methods) and "soft" attention (trained end-to-end) mechanism, and show qualitatively that the decoder focuses on sensible regions while generating text, adding an additional layer of interpretability to the model. The attention-based models achieve state-of-the art on Flickr8k, Flickr30 and MS Coco.

#### Key Points

- To find image correspondence use lower convolutional layers to attend to.
- Two attention mechanisms: Soft and hard. Depending on evaluation metric (BLEU vs. METERO) one or the other performs better.
- Largest data set (MS COCO) takes 3 days to train on Titan Black GPU. Oxford VGG.
- Soft attention is same as for seq2seq models.
- Attention weights are visualized by upsampling and applying a Gaussian

#### Notes/Questions

- Would've liked to see an explanation of when/how soft vs. hard attention does better.
- What is the computational overhead of using the attention mechanism? Is it significant?

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)

This paper is about transfer learning for computer vision tasks.

## Contributions
* Before this paper, people focused on similar datasets (e.g. ImageNet-like images) or even the same dataset but a different task (classification -> segmentation). This paper, they look at extremely different dataset (ImageNet-like vs text) but only one task (classification). They show that all layers can be shared (including the last classification layer) between datasets such as MNIST and CIFAR-10
* Normalizing information is necessary for sharing models between datasets in order to compensate for dataset-specific differences. Domain-specific scaling parameters work well.

## Evaluation

* Used datasets:
  1. MNIST (10 classes: handwritten digits 0-9),
  2. SVHN (10 classes: house number digits, 0-9),
  3. [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (10 classes: airplane, automobile, bird, ...)
  4. Daimler Mono Pedestrian Classification Benchmark (18 × 36 pixels)
  5. Human Sketch dataset (20000 human sketches of every day objects such as “book”, “car”, “house”, “sun”)
  6. German Traffic Sign Recognition (GTSR) Benchmark (43 traffic signs)
  7. Plankton imagery data (classification benchmark that contains 30336 images of various organisms ranging from the smallest single-celled protists to copepods, larval fish, and larger jellies)
  8. Animals with Attributes (AwA): 30475 images of 50 animal species (for zero-shot learning)
  9. Caltech-256: object classification benchmark (256 object categories and an additional background class)
  10. Omniglot: 1623 different handwritten characters from 50 different alphabets (one shot learning)
* images are resized to 64 × 64 pixels, greyscale ones are converted into RGB by setting the three channels to the same value
* Each dataset is also whitened, by subtracting its mean and dividing it by its standard deviation per channel
* **Architecture**: ResNet + Global Average Pooling + FC with Softmax
* "As the majority of the datasets have a different number of classes, we use a dataset-specific fully connected layer in our experiments unless otherwise stated."
* **Data augmentation**: We follow the same data augmentation strategy in [[18]](http://www.shortscience.org/paper?bibtexKey=journals/corr/HeZRS15), the 64 × 64 size whitened image is padded with 8 pixels on all sides and a 64×64 patch randomly sampled from the padded image or its horizontal flip (except for MNIST / Omniglot / SVHN, as those contain text)
* **Training**: stochastic gradient descent with momentum

Sharing strategies:

1. Baseline: Train networks for each dataset independantly
2. Full sharing: For MNIST / SVHN / CIFAR-10, group classes randomly together so that Node 2 might be digit "7" for MNIST, digit "3" for SVHN and "aeroplane" for CIFAR-10. They are trained together in one network.
3. Deep sharing: Share all layers except the last one. Use all 10 datasets for this.
4. Partial sharing: Have a dataset-specific first part to compensate for different image statistics, but share the middle of the network.

The results seem to be inconclusive to me.


## Follow-up / related work

Teye et al. show that neural networks with batch normalization can be used to give uncertainty estimates through Monte Carlo sampling. In particular, instead of using the test mode of batch normalization, where the statistics (mean and variance) of each batch normalization layer are fixed, these statistics are computed per batch, as in training mode. To this end, for a specific query image, random batches from the training set are sampled, and prediction uncertainty is estimated using Monte Carlo sampling to compute mean and variance. This is summarized in Algorithm 1, depicting the proposed Monte Carlo Batch Normalization method. In the paper, this approach is further interpreted as approximate inference in Bayesian models.

https://i.imgur.com/nRdOvzs.jpg
Algorithm 1: Monte Carlo approach for using batch normalization for uncertainty estimation.

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