What the paper is about:
KeypointNet learns the optimal set of 3D keypoints and their 2D detectors for a specified downstream task. The authors demonstrate this by extracting 3D keypoints and their 2D detectors for the task of relative pose estimation across views. They show that, using keypoints extracted by KeypointNet, relative pose estimates are superior to ones that are obtained from a supervised set of keypoints.


Approach:
Training samples for KeypointNet comprise two views (images) of an object. The task is to then produce an ordered list of 3D keypoints that, upon orthogonal procrustes alignment, produce the true relative 3D pose across those views. The network has N heads, each of which extracts one (3D) keypoint (from a 2D image). There are two primary loss terms. A multi-view consistency loss measures the discrepancy between the two sets of extracted keypoints under the ground-truth transform. A relative-pose estimation loss penalizes the angular discrepency (under orthogonal procrustes) of the estimated transform using the extracted keypoints vs the GT transform. Additionally, they require keypoints to be distant from each other, and to lie within the object silhouette.

This paper proposes the use of pretrained convolutional neural networks that have already learned to encode semantic information as loss functions for training networks for style transfer and super-resolution. The trained networks corresponding to selected style images are capable of performing style transfer for any content image with a single forward pass (as opposed to explicit optimization over output image) achieving as high as 1000x speedup and similar qualitative results as Gatys et al. Key contributions:

- Image transformation network
    - Convolutional neural network with residual blocks and strided & fractionally-strided convolutions for in-network downsampling and upsampling. 
    - Output is the same size as input image, but rather than training the network with a per-pixel loss, it is trained with a feature reconstruction perceptual loss.

- Loss network
    - VGG-16 with frozen weights 
    - Feature reconstruction loss: Euclidean distance between feature representations
    - Style reconstruction loss: Frobenius norm of the difference between Gram matrices, performed over a set of layers.

- Experiments
    - Similar objective values and qualitative results as explicit optimization over image as in Gatys et al for style transfer
    - For single-image super-resolution, feature reconstruction loss reconstructs fine details better and 'looks' better than a per-pixel loss, even though PSNR values indicate otherwise. Respectable results in comparison to SRCNN.

## Weaknesses / Notes

- Although fast, limited by styles at test-time (as opposed to iterative optimizer that is limited by speed and not styles). Ideally, there should be a way to feed in style and content images, and do style transfer with a single forward pass.

Catastrophic forgetting is the tendency of an neural network to forget previously learned information when learning new information. This paper combats that by keeping a buffer of experience and applying meta-learning to it. They call their new module Meta Experience Replay or MER.

How does this work? At each update they compute multiple possible updates to the model weights. One for the new batch of information and some more updates for batches of previous experience. Then they apply  meta-learning using the REPTILE algorithm, here the meta-model sees each possible update and has to predict the output which combines them with the least interference. This is done by predicting an update vector that maximizes the dot product between the new and old update vectors, that way it transfers as much learning as possible from the new update without interfering with the old updates. https://i.imgur.com/TG4mZOn.png

Does it work? Yes, while it may take longer to train, the results show that it generalizes better and needs a much smaller buffer of experience than the popular approach of using replay buffers.

Borg is Google's cluster manager. Users submit jobs, a collection of tasks, to Borg which are then run in a single cell, many of which live inside a single cluster. Borg jobs are either high priority latency-sensitive production jobs (e.g. user facing products and core infrastructure) or low priority non-production batch jobs. Jobs have typical properties like name and owner and can also express constraints (e.g. only run on certain architectures). Tasks also have properties and state their resource demands. Borg jobs are specified in BCL and are bundled as statically linked executables. Jobs are labeled with a priority and must operate within quota limits. Resources are bundled into allocs in which multiple tasks can run. Borg also manages a naming service, and exports a UI called Sigma to developers.

Cells are managed by five-way replicated Borgmasters. A Borgmaster communicates with Borglets running on each machine via RPC, manages the Paxos replicated state of system, and exports information to Sigma. There is also a high fidelity borgmaster simulator known as the Fauxmaster which can used for debugging.

One subcomponent of the Borgmaster handles scheduling. Submitted jobs are placed in a queue and scheduled by priority and round-robin within a priority. Each job undergoes feasibility checking where Borg checks that there are enough resources to run the job and then scoring where Borg determines the best place to run the job. Worst fit scheduling spreads jobs across many machines allowing for spikes in resource usage. Best fit crams jobs as closely as possible which is bad for bursty loads. Borg uses a scheduler which attempts to limit "stranded resources": resources on a machine which cannot be used because other resources on the same machine are depleted. Tasks that are preempted are placed back on the queue. Borg also tries to place jobs where their packages are already loaded, but offers no other form of locality.

Borglets run on each machine and are responsible for starting and stopping tasks, managing logs, and reporting to the Borgmaster. The Borgmaster periodically polls the Borglets (as opposed to Borglets pushing to the Borgmaster) to avoid any need for flow control or recovery storms.

The Borgmaster performs a couple of tricks to achieve high scalability.

 - The scheduler operates on slightly stale state, a form of "optimistic scheduling".
 - The Borgmaster caches job scores.
 - The Borgmaster performs feasibility checking and scoring for all equivalent jobs at once.
 - Complete scoring is hard, so the Borgmaster uses randomization.

The Borgmaster puts the onus of fault tolerance on applications, expecting them to handle occasional failures. Still, the Borgmaster also performs a set of nice tricks for availability.

 - It reschedules evicted tasks.
 - It spreads tasks across failure domains.
 - It limits the number of tasks in a job that can be taken down due to maintenance.
 - Avoids past machine/task pairings that lead to failure.

To measure cluster utilization, Google uses a cell compaction metric: the smallest a cell can be to run a given workload. Better utilization leads directly to savings in money, so Borg is very focused on improving utilization. For example, it allows non-production jobs to reclaim unused resources from production jobs.

Borg uses containers for isolation. It also makes sure to throttle or kill jobs appropriately to ensure performance isolation.

This paper proposed a method to locate an object based on an image and a sentence describing objects in the image. Then, predicting a new visual concept embedding based on two graphs (1) a graph that describes the relationship between objects in a supplemental sentence describing several objects, and (2) a graph that describes the relationship between the detected object in the image and example images related to objects in the supplementary sentence. This embedding can be used for many downstream tasks such as Visual entailment, Visual Reasoning.

For example, in the domain 1 image, the new visual concept is red, and the model can locate where is the red cub in the image (1a). Then, in (1b) the model can interpret the supplemental sentences that relate the novel concept with other concepts.

https://i.imgur.com/yBIteYT.png

To locate the box that describes the object, this work utilized MaskRCNN to first detect the object in the scene, then used a Neuro-Symbolic Program to match the object mentioned in the input sentence with the detected objects by MaskRCNN.

https://i.imgur.com/2cG9IUX.png

To learn the concept embedding for that object, this work needs a supplemental sentence that describes several objects, they are known concepts except one is a novel concept. Then, building two graphs $GNN_{concept}$, and $GNN_{example}$.

In $GNN_{concept}$, this is a graph representing the relationship between known concepts and the novel concept.
For example in this graph, "White-eyed Vireo" is a new concept.
https://i.imgur.com/1LjirJz.png

In $GNN_{example}$, this is a graph representing the relationship between the detected object that represents the novel object and example images of the novel object.
https://i.imgur.com/SdR74Vu.png

Then learn the concept embedding for this novel concept.

https://i.imgur.com/YGYEPvc.png

https://i.imgur.com/VXOBn6n.png