Stereo Basic Stereo algorithm can be formulated as Markov Random Field. Thus Methods in MRF inference could all be used. Prior Planar Prior Natural scene is usually piece-wise! How to impose this idea to depth map?
Semantics Vision Task Note semantics and geometric reasoning is conceptually similar to each other Stereo and Optical flow is about finding correspondence / matches. Object recognition in some sense is finding correspondence w.r.t. a template, and make the template match the observation. Semantic Vision before CNN So ancient semantic detector works like this
TOC {:toc} Continuing Image Prior and Generative Model . Probabilistic Graphic Model comes into scene, when we want to model and deal with some complex distribution over many variables. When we start to add structure into the model, not everything depend on everything, then the dependency relationship among variables emerges as a graph structure.
Motivation Major Reference Zeroth order optimization, or derivative free optimization is also known as the oracle problem. It’s nothing new to optimization community. Interest in ZOO algorithm resurges partly because it could be used in black box adversarial attack, if the softmax probability is given; and it could also be used in optimization of experimental output; and it could also be used for many design problem as the result has a non-analytical relationship with the parameters.
Note on Advanced Computer Vision This is the course note for Advanced Computer Vision Class (CS 659a) These are links to notes for individual modules and specific domain notes. Basic Computer Vision
Image Prior: Modeling Spatial Relaionship Materials: https://www.cse.wustl.edu/~ayan/courses/cse659a/lec1.html#/ TOC {:toc} This is the basis for most further applications We need Regularizer for a spatial configuration $$\hat X=\arg\min_X \phi(X)+R(X)\\$$This could be interpreted in a Bayesian way,
人间失格是一本高中时就熟知的书，可是也许是一直没有心绪去读，最近才在飞机上认真读完。没想到的是，并没有像以前期待的那样会被这个人和这个故事拉进黑暗的深渊，心情也并不十分抑郁。只是在有些主人公对他身边的女人不善时才会生气或者难过，其余时候对主人公还是很同情的。 完全可以想象，有些人会十分反感和厌恶主人公，甚至作者自己在序言三张照片中，也表现出了这种"自我"厌恶。我并不那么讨厌他，虽然也有哀其不幸怒其不争的心情，但不可否认主人公的聪明以及有吸引力，而且在这之上对生活有种过早也过于清醒的看法。所以此次读完更想去分辨的是，他清醒地看出了什么，以及哪些是他自己造成的困境。 文中有一段话能很好的概括主人公的性格悲剧， 我的不幸乃是一个缺乏拒绝能力的人的不幸。我时常陷入一种恐惧之中，以为如果别人劝我干什么而自己加以拒绝的话，就会在对方的心灵和自己的心灵中剜开一道永远无法修复的裂痕 无法拒绝别人，太会讨好他人自然是这个悲剧的动因之一。从CogSci角度，一个人很早拥有了theory of mind能识别他人的动机与欲望似乎是好事，但这很容易就会产生一个reward feedback—- 如果你满足别人的期待就会被reward反之punish, and that’s the definition of 讨好。可以想象，这样的reinforcement learning 可以在人早期性格上留下多大的痕迹。 有人说 如果一个人评判自我的超我力量太强，就会时常陷入自责中，并因此稍稍做的对不起别人 或者惹人生气，都会是巨大的灾难。 人间失格的主人公与加缪局外人的主人公有着很值得分析的相似与不同。 他们都是社会的某种局外人，跟任何团体保持着疏离的关系。 对社会的一般道德原则和现状有些怀疑。 某种意义上都讨女人喜欢。 然而加缪的主人公，在看清看透了这个世界之后会更全面的反叛他。其一是生活不需要接济帮助，独立自主。其二是不会像阿叶一般强烈的自责，直到最后都没怎么自责，而对世界充满了控诉。
Understanding of Adversarial Attack Why we should care adversarial examples? Why they are surprising? Adversarial example and feature visualization are related intrinsically. Basic algorithm underlying both is to generate image that activate a unit most. For Adversarial attack, the point is to change the image in an in-perceptible way but change the network output dramatically. On the other hand the feature visualization is to generate a perceptually meaningful pattern that represent a natural image that the unit like.
Krylov Subspace, Lancosz Iteration, QR and Conjugate Gradient Motivation In practise, many numerical algorithms include iteratively multiply a matrix, like power method and QR algorithm. All these algorithms have their core connected to a single construct, Krylov subspace and a operation, Lancosz Iteration. So this note motivates to understand this core.
Objective Here I want to compare several common deep learning frameworks and make sense of their workflow. Core Logic Tensorflow General Comments: TF is more like a library, in which many low-level operations are defined and programs are long. In contrast, Keras which can use tensorflow as backend has the similar level of abstraction as PyTorch, which is a higher level deep learning package. TFLearn may also be a higher level wrapper.