Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to learn about the structure of a domain - remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world. We describe a predictive neural network (*PredNet*) architecture that is inspired by the concept of *predictive coding* from the neuroscience literature. These networks learn to predict future frames in a video sequence, with each layer in the network making local predictions and only forwarding deviations from those predictions to subsequent network layers. We show that these networks are able to robustly learn to predict the movement of synthetic (rendered) objects, and that in doing so, the networks learn internal representations that are useful for decoding latent object parameters (e.g. pose) that support object recognition with fewer training views. We also show that these networks can scale to complex natural image streams (car-mounted camera videos), capturing key aspects of both egocentric movement and the movement of objects in the visual scene, and the representation learned in this setting is useful for estimating the steering angle. These results suggest that prediction represents a powerful framework for unsupervised learning, allowing for implicit learning of object and scene structure.
Learning about the physical structure and semantics of the world from video (without supervision) is a very hot area in computer vision and machine learning. In this paper, the authors investigate how the prediction of future image frames (inherently unsupervised) can help to deduce object/s structure and it*s properties (in this case single object pose, category, and steering angle, (after a supervised linear readout step)) I enjoyed reading this paper, it is clear, interesting and proposes an original network architecture (PredNet) for video frame prediction that has produced promising results on both synthetic and natural images. Moreover, the extensive experimental evaluation and analysis the authors provide puts it on solid ground to which others can compare. The weaknesses: - the link to predictive coding should be better explained in the paper if it is to be used as a motivation for the prednet model. - any idea that the proposed method is learning an implicit `model* of the `objects* that make up the `scene* is vague and far fetched, but it sounds great. Minor comment: Next to the number of labeled training examples (Fig.5), it would be interesting to see how much unsupervised training data was used to train your representations.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to learn about the structure of a domain - remains a difficult unsolved challenge. Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world. We describe a predictive neural network (*PredNet*) architecture that is inspired by the concept of *predictive coding* from the neuroscience literature. These networks learn to predict future frames in a video sequence, with each layer in the network making local predictions and only forwarding deviations from those predictions to subsequent network layers. We show that these networks are able to robustly learn to predict the movement of synthetic (rendered) objects, and that in doing so, the networks learn internal representations that are useful for decoding latent object parameters (e.g. pose) that support object recognition with fewer training views. We also show that these networks can scale to complex natural image streams (car-mounted camera videos), capturing key aspects of both egocentric movement and the movement of objects in the visual scene, and the representation learned in this setting is useful for estimating the steering angle. These results suggest that prediction represents a powerful framework for unsupervised learning, allowing for implicit learning of object and scene structure.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



Learning about the physical structure and semantics of the world from video (without supervision) is a very hot area in computer vision and machine learning. In this paper, the authors investigate how the prediction of future image frames (inherently unsupervised) can help to deduce object/s structure and it*s properties (in this case single object pose, category, and steering angle, (after a supervised linear readout step)) I enjoyed reading this paper, it is clear, interesting and proposes an original network architecture (PredNet) for video frame prediction that has produced promising results on both synthetic and natural images. Moreover, the extensive experimental evaluation and analysis the authors provide puts it on solid ground to which others can compare. The weaknesses: - the link to predictive coding should be better explained in the paper if it is to be used as a motivation for the prednet model. - any idea that the proposed method is learning an implicit `model* of the `objects* that make up the `scene* is vague and far fetched, but it sounds great. Minor comment: Next to the number of labeled training examples (Fig.5), it would be interesting to see how much unsupervised training data was used to train your representations.
assistant
Learning about the physical structure and semantics of the world from video (without supervision) is a very hot area in computer vision and machine learning. In this paper, the authors investigate how the prediction of future image frames (inherently unsupervised) can help to deduce object/s structure and it*s properties (in this case single object pose, category, and steering angle, (after a supervised linear readout step)) I enjoyed reading this paper, it is clear, interesting and proposes an original network architecture (PredNet) for video frame prediction that has produced promising results on both synthetic and natural images. Moreover, the extensive experimental evaluation and analysis the authors provide puts it on solid ground to which others can compare. The weaknesses: - the link to predictive coding should be better explained in the paper if it is to be used as a motivation for the prednet model. - any idea that the proposed method is learning an implicit `model* of the `objects* that make up the `scene* is vague and far fetched, but it sounds great. Minor comment: Next to the number of labeled training examples (Fig.5), it would be interesting to see how much unsupervised training data was used to train your representations.