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- ShortScience.org is a platform for post-publication discussion aiming to improve accessibility and reproducibility of research ideas.
- The website has 1583 public summaries, mostly in machine learning, written by the community and organized by paper, conference, and year.
- Reading summaries of papers is useful to obtain the perspective and insight of another reader, why they liked or disliked it, and their attempt to demystify complicated sections.
- Also, writing summaries is a good exercise to understand the content of a paper because you are forced to challenge your assumptions when explaining it.
- Finally, you can keep up to date with the flood of research by reading the latest summaries on our Twitter and Facebook pages.

View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose

Liu, Ting and Sun, Jennifer J. and Zhao, Long and Zhao, Jiaping and Yuan, Liangzhe and Wang, Yuxiao and Chen, Liang-Chieh and Schroff, Florian and Adam, Hartwig

arXiv e-Print archive - 2020 via Local Bibsonomy

Keywords: dblp

Liu, Ting and Sun, Jennifer J. and Zhao, Long and Zhao, Jiaping and Yuan, Liangzhe and Wang, Yuxiao and Chen, Liang-Chieh and Schroff, Florian and Adam, Hartwig

arXiv e-Print archive - 2020 via Local Bibsonomy

Keywords: dblp

[link]
The goal of this paper is to learn a model that embeds 2D keypoints(the locations of specific key body parts in 2D space) representing a particular pose into a vector embedding where nearby points in embedding space are also nearby in 3D space. This sort of model is useful because the same 3D pose can generate a wide variety of 2D pose projections, and it can be useful to learn which apparently-distinct representations actually map to the same 3D pose. To do this, the basic approach used by the authors (with has a few variants), is - Take a dataset of 3D poses, and corresponding 2D projections - Define a notion of "matching" 3D poses, based on a parameter kappa, which designates the maximum average per-joint distance at which two 3D poses can be considered the same - Construct triplets composed of an anchor pose, a "positive" pose (a different 2D pose with a matching 3D pose), and a "negative" pose (some other 2D pose sampled from the dataset using a strategy that explicitly seeks out hard negative examples) - Calculate a triplet loss, that pushes positive examples closer together, and pulls negative examples farther apart. This is specifically done by defining a probabilistic representation of p(match | z1, z2), or, the probability of a match in 3D space given the embeddings of the two 2D poses. This is parametrized using a sigmoid with trainable parameters, as shown below https://i.imgur.com/yFCCVuA.png - They they calculate a distance kernel as the negative log of that probability, and calculate the basic triplet loss, which tries to maximize the diff between the the distance between negative examples, and the distance between positive examples. - They also add an additional loss further incentivizing the match probability to be higher on the positive pair (in addition to just pushing the positive and negative pair further apart) - The final loss is a Gaussian prior loss, incentivizing the learned embeddings z to be in the shape of a Gaussian https://i.imgur.com/SxvcvJG.png This represents the central shape of the method. Some additional ablations include: - Camera Augmentation: Creational additional triplets by taking existing 3D poses and generating artificial pairs of 2D poses at different camera views - Temporal Pose Embedding - Embedding multiple temporally connected pose, rather than just a single one - Keypoint Dropout - To simulate situations where some keypoints are occluded, the authors tried training with some keypoints dropped out, either keypoints selected at random, or selected jointly and non-independently based on a model of which keypoints are likely to be occluded together The authors found that their method was generally quite a bit stronger that prior approaches for the task of querying similar 3D poses given a 2D pose input, including some alternate methods that do direct 3D estimation. |

Nerfies: Deformable Neural Radiance Fields

Keunhong Park and Utkarsh Sinha and Jonathan T. Barron and Sofien Bouaziz and Dan B Goldman and Steven M. Seitz and Ricardo Martin-Brualla

arXiv e-Print archive - 2020 via Local arXiv

Keywords: cs.CV, cs.GR

**First published:** 2023/03/27 (just now)

**Abstract:** We present the first method capable of photorealistically reconstructing
deformable scenes using photos/videos captured casually from mobile phones. Our
approach augments neural radiance fields (NeRF) by optimizing an additional
continuous volumetric deformation field that warps each observed point into a
canonical 5D NeRF. We observe that these NeRF-like deformation fields are prone
to local minima, and propose a coarse-to-fine optimization method for
coordinate-based models that allows for more robust optimization. By adapting
principles from geometry processing and physical simulation to NeRF-like
models, we propose an elastic regularization of the deformation field that
further improves robustness. We show that our method can turn casually captured
selfie photos/videos into deformable NeRF models that allow for photorealistic
renderings of the subject from arbitrary viewpoints, which we dub "nerfies." We
evaluate our method by collecting time-synchronized data using a rig with two
mobile phones, yielding train/validation images of the same pose at different
viewpoints. We show that our method faithfully reconstructs non-rigidly
deforming scenes and reproduces unseen views with high fidelity.
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Keunhong Park and Utkarsh Sinha and Jonathan T. Barron and Sofien Bouaziz and Dan B Goldman and Steven M. Seitz and Ricardo Martin-Brualla

arXiv e-Print archive - 2020 via Local arXiv

Keywords: cs.CV, cs.GR

[link]
This summary builds substantially on my summary of NERFs, so if you haven't yet read that, I recommend doing so first! The idea of a NERF is learn a neural network that represents a 3D scene, and from which you can, once the model is trained, sample an image of that scene from any desired angle. This involves structuring your neural network as a function that predicts the RGB color and density/opacity for a given point in 3D space (x, y, z), from a given viewing angle (theta, phi). With such a function, you can generate predictions of what images taken from certain angles would look like by sampling along a viewing ray, and integrating the combined hue and opacity into an aggregated view. This prediction can then be compared to a true image taken from that direction, and gradients passed backwards into the prediction model. An important assumption of this model is that the scene being photographed is static; specifically, that every point in space is always inhabited by the same part of the 3D object, regardless of what angle it's viewed from. This is a reasonable assumption for photos of inanimate objects, or of humans in highly controlled lab settings, but it is often not true for humans when you, say, ask them to take a selfie video of themselves. Even if they're trying to keep roughly still, there will be slight shifts in the location and position of their head between frames, and the authors of this paper show that this can lead to strange artifacts if you naively try to train a NERF from the images (including a particularly odd one where it hallucinates tiny copies of the image in the air surrounding the face). https://i.imgur.com/IUVh6uM.png The fix proposed by this paper is to apply a learnable deformation field to each image, where the notion is to deform each view into being in one canonical position (fixed per network, since, again, one network corresponds to a single scene). This means that, along with learning the parameters of the NERF itself, you're also learning what deformation to apply to each training image to get it into this canonical position. This is done by parametrizing the deformation in a particular way, and then having that deformation be conditioned by a latent vector that's trained similar to how you'd train an embedding (one learned vector per image example). The parametrization of the deformation is honestly a little bit over my head, given my lack of grounding in 3D modeling, but my general sense is that it applies some constraints and regularization to ensure that the learned deformations are realistic, insofar as humans are mostly rigid (one patch of skin on my forehead generally doesn't move except in concordance with the rest of my forehead), but with some possibility for elasticity (skin can stretch if I, say, smile). The authors also include an annealing scheme whereby, early in training, the model focuses on learning course (large-scale) deformations, and later in training, it's allowed to also learn weights for more precise deformations. This is to hopefully match macro-scale shifts before adding the noise of precise changes. This addition of a learned deformation is most of the contribution of this method: with it applied, they show that they're able to learn realistic NERFs from selfies, which they term "NERFIES". They mention a few pieces of concurrent work that try to solve the same problem of non-static human subjects in different ways, but I haven't had a chance to read those, so I can't really comment on how NERFIES stacks up to alternate approaches, but it appears to be as least one empirically convincing solution to the problem it's aiming at. |

Communication-Efficient Learning of Deep Networks from Decentralized Data

McMahan, H. Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and Arcas, Blaise Agüera y

- 2016 via Local Bibsonomy

Keywords: distributed, deep_learning, hpc

McMahan, H. Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and Arcas, Blaise Agüera y

- 2016 via Local Bibsonomy

Keywords: distributed, deep_learning, hpc

[link]
Federated learning is the problem of training a model that incorporates updates from the data of many individuals, without having direct access to that data, or having to store it. This is potentially desirable both for reasons of privacy (not wanting to have access to private data in a centralized way), and for potential benefits to transport cost when data needed to train models exists on a user's device, and would require a lot of bandwidth to transfer to a centralized server. Historically, the default way to do Federated Learning was with an algorithm called FedSGD, which worked by: - Sending a copy of the current model to each device/client - Calculating a gradient update to be applied on top of that current model given a batch of data sampled from the client's device - Sending that gradient back to the central server - Averaging those gradients and applying them all at once to a central model The authors note that this approach is equivalent to one where a single device performs a step of gradient descent locally, sends the resulting *model* back to the the central server, and performs model averaging by averaging the parameter vectors there. Given that, and given their observation that, in federated learning, communication of gradients and models is generally much more costly than the computation itself (since the computation happens across so many machines), they ask whether the communication required to get to a certain accuracy could be better optimized by performing multiple steps of gradient calculation and update on a given device, before sending the resulting model back to a central server to be average with other clients models. Specifically, their algorithm, FedAvg, works by: - Dividing the data on a given device into batches of size B - Calculating an update on each batch and applying them sequentially to the starting model sent over the wire from the server - Repeating this for E epochs Conceptually, this should work perfectly well in the world where data from each batch is IID - independently drawn from the same distribution. But that is especially unlikely to be true in the case of federated learning, when a given user and device might have very specialized parts of the data space, and prior work has shown that there exist pathological cases where averaged models can perform worse than either model independently, even *when* the IID condition is met. The authors experiment empirically ask the question whether these sorts of pathological cases arise when simulating a federated learning procedure over MNIST and a language model trained on Shakespeare, trying over a range of hyperparameters (specifically B and E), and testing the case where data is heavily non-IID (in their case: where different "devices" had non-overlapping sets of digits). https://i.imgur.com/xq9vi8S.png They show that, in both the IID and non-IID settings, they are able to reach their target accuracy, and are able to do so with many fewer rounds of communciation than are required by FedSGD (where an update is sent over the wire, and a model sent back, for each round of calculation done on the device.) The authors argue that this shows the practical usefulness of a Federated Learning approach that does more computation on individual devices before updating, even in the face of theoretical pathological cases. |

Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen

arXiv e-Print archive - 2018 via Local arXiv

Keywords: cs.CV

**First published:** 2018/01/13 (5 years ago)

**Abstract:** In this paper we describe a new mobile architecture, MobileNetV2, that
improves the state of the art performance of mobile models on multiple tasks
and benchmarks as well as across a spectrum of different model sizes. We also
describe efficient ways of applying these mobile models to object detection in
a novel framework we call SSDLite. Additionally, we demonstrate how to build
mobile semantic segmentation models through a reduced form of DeepLabv3 which
we call Mobile DeepLabv3.
The MobileNetV2 architecture is based on an inverted residual structure where
the input and output of the residual block are thin bottleneck layers opposite
to traditional residual models which use expanded representations in the input
an MobileNetV2 uses lightweight depthwise convolutions to filter features in
the intermediate expansion layer. Additionally, we find that it is important to
remove non-linearities in the narrow layers in order to maintain
representational power. We demonstrate that this improves performance and
provide an intuition that led to this design. Finally, our approach allows
decoupling of the input/output domains from the expressiveness of the
transformation, which provides a convenient framework for further analysis. We
measure our performance on Imagenet classification, COCO object detection, VOC
image segmentation. We evaluate the trade-offs between accuracy, and number of
operations measured by multiply-adds (MAdd), as well as the number of
parameters
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Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen

arXiv e-Print archive - 2018 via Local arXiv

Keywords: cs.CV

[link]
This work expands on prior techniques for designing models that can both be stored using fewer parameters, and also execute using fewer operations and less memory, both of which are key desiderata for having trained machine learning models be usable on phones and other personal devices. The main contribution of the original MobileNets paper was to introduce the idea of using "factored" decompositions of Depthwise and Pointwise convolutions, which separate the procedures of "pull information from a spatial range" and "mix information across channels" into two distinct steps. In this paper, they continue to use this basic Depthwise infrastructure, but also add a new design element: the inverted-residual linear bottleneck. The reasoning behind this new layer type comes from the observation that, often, the set of relevant points in a high-dimensional space (such as the 'per-pixel' activations inside a conv net) actually lives on a lower-dimensional manifold. So, theoretically, and naively, one could just try to use lower dimensional internal representations to map the dimensionality of that assumed manifold. However, the authors argue that ReLU non-linearities kill information (because of the region where all inputs are mapped to zero), and so having layers contain only the number of dimensions needed for the manifold would mean that you end up with too-few dimensions after the ReLU information loss. However, you need to have non-linearities somewhere in the network in order to be able to learn complex, non-linear functions. So, the authors suggest a method to mostly use smaller-dimensional representations internally, but still maintain ReLus and the network's needed complexity. https://i.imgur.com/pN4d9Wi.png - A lower-dimensional output is "projected up" into a higher dimensional output - A ReLu is applied on this higher-dimensional layer - That layer is then projected down into a smaller-dimensional layer, which uses a linear activation to avoid information loss - A residual connection between the lower-dimensional output at the beginning and end of the expansion This way, we still maintain the network's non-linearity, but also replace some of the network's higher-dimensional layers with lower-dimensional linear ones |

Discovering Reinforcement Learning Algorithms

Junhyuk Oh and Matteo Hessel and Wojciech M. Czarnecki and Zhongwen Xu and Hado van Hasselt and Satinder Singh and David Silver

arXiv e-Print archive - 2020 via Local arXiv

Keywords: cs.LG, cs.AI

**First published:** 2023/03/27 (just now)

**Abstract:** Reinforcement learning (RL) algorithms update an agent's parameters according
to one of several possible rules, discovered manually through years of
research. Automating the discovery of update rules from data could lead to more
efficient algorithms, or algorithms that are better adapted to specific
environments. Although there have been prior attempts at addressing this
significant scientific challenge, it remains an open question whether it is
feasible to discover alternatives to fundamental concepts of RL such as value
functions and temporal-difference learning. This paper introduces a new
meta-learning approach that discovers an entire update rule which includes both
'what to predict' (e.g. value functions) and 'how to learn from it' (e.g.
bootstrapping) by interacting with a set of environments. The output of this
method is an RL algorithm that we call Learned Policy Gradient (LPG). Empirical
results show that our method discovers its own alternative to the concept of
value functions. Furthermore it discovers a bootstrapping mechanism to maintain
and use its predictions. Surprisingly, when trained solely on toy environments,
LPG generalises effectively to complex Atari games and achieves non-trivial
performance. This shows the potential to discover general RL algorithms from
data.
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Junhyuk Oh and Matteo Hessel and Wojciech M. Czarnecki and Zhongwen Xu and Hado van Hasselt and Satinder Singh and David Silver

arXiv e-Print archive - 2020 via Local arXiv

Keywords: cs.LG, cs.AI

[link]
This work attempts to use meta-learning to learn an update rule for a reinforcement learning agent. In this context, "learning an update rule" means learning the parameters of an LSTM module that takes in information about the agent's recent reward and current model and outputs two values - a scalar and a vector - that are used to update the agent's model. I'm not going to go too deep into meta-learning here, but, at a high level, meta learning methods optimize parameters governing an agent's learning, and, over the course of many training processes over many environments, optimize those parameters such that the reward over the full lifetime of training is higher. To be more concrete, the agent in a given environment learns two things: - A policy, that is, a distribution over predicted action given a state. - A "prediction vector". This fits in the conceptual slot where most RL algorithms would learn some kind of value or Q function, to predict how much future reward can be expected from a given state. However, in this context, this vector is *very explicitly* not a value function, but is just a vector that the agent-model generates and updates. The notion here is that maybe our human-designed construction of a value function isn't actually the best quantity for an agent to be predicting, and, if we meta-learn, we might find something more optimal. I'm a little bit confused about the structure of this vector, but I think it's *intended* to be a categorical 1-of-m prediction At each step, after acting in the environment, the agent passes to an LSTM: - The reward at the step - A binary of whether the trajectory is done - The discount factor - The probability of the action that was taken from state t - The prediction vector evaluated at state t - The prediction vector evaluated at state t+1 Given that as input (and given access to its past history from earlier in the training process), the LSTM predicts two things: - A scalar, pi-hat - A prediction vector, y-hat These two quantities are used to update the existing policy and prediction model according to the rule below. https://i.imgur.com/xx1W9SU.png Conceptually, the scalar governs whether to increase or decrease probability assigned to the taken action under the policy, and y-hat serves as a target for the prediction vector to be pulled towards. An important thing to note about the LSTM structure is that none of the quantities it takes as input are dependent on the action or observation space of the environment, so, once it is learned it can (hopefully) generalize to new environments. Given this, the basic meta learning objective falls out fairly easily - optimize the parameters of the LSTM to maximize lifetime reward, taken in expectation over training runs. However, things don't turn out to be quite that easy. The simplest version of this meta-learning objective is wildly unstable and difficult to optimize, and the authors had to add a number of training hacks in order to get something that would work. (It really is dramatic, by the way, how absolutely essential these are to training something that actually learns a prediction vector). These include: - A entropy bonus, pushing the meta learned parameters to learn policies and prediction vectors that have higher entropy (which is to say: are less deterministic) - An L2 penalty on both pi-hat and y-hat - A removal of the softmax that had originally been originally taken over the k-dimensional prediction vector categorical, and switching that target from a KL divergence to a straight mean squared error loss. As far as I can tell, this makes the prediction vector no longer actually a 1-of-k categorical, but instead just a continuous vector, with each value between 0 and 1, which makes it make more sense to think of k separate binaries? This I was definitely confused about in the paper overall https://i.imgur.com/EL8R1yd.png With the help of all of these regularizers, the authors were able to get something that trained, and that appeared to be able to perform comparably to or better than A2C - the human-designed baseline - across the simple grid-worlds it was being trained in. However, the two most interesting aspects of the evaluation were: 1. The authors showed that, given the values of the prediction vector, you could predict the true value of a state quite well, suggesting that the vector captured most of the information about what states were high value. However, beyond that, they found that the meta-learned vector was able to be used to predict the value calculated with discount rates different that than one used in the meta-learned training, which the hand-engineered alternative, TD-lambda, wasn't able to do (it could only well-predict values at the same discount rate used to calculate it). This suggests that the network really is learning some more robust notion of value that isn't tied to a specific discount rate. 2. They also found that they were able to deploy the LSTM update rule learned on grid worlds to Atari games, and have it perform reasonably well - beating A2C in a few cases, though certainly not all. This is fairly impressive, since it's an example of a rule learned on a different, much simpler set of environments generalizing to more complex ones, and suggests that there's something intrinsic to Reinforcement Learning that it's capturing |

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