Note on Patch Based Shape Interpretation These are 2 related papers both employ a patch based approach to tackle shape from shading problem. Typically patches have simpler appearance, thus they are easier to collect the statistics on or fit a model on. The spirit is to find a local explanation for patches in an image. However, as there will be ambiguity in local patches, the algorithm should not over-commiting to any one of the explanation and keep the distribution of possible shapes. And then take these local shape proposals and see which can stitch together and make sense globally.
Note on Categorization and Concepts From lecture notes from Science of Behavior Configuration The relative configuration of a single elements Example: Face What defines a face? Components Essential feature Configural property Relative Invariance to many change in Stimuli
Some Computation on Sphere (Updating) Motivation Recently, in research, we encounter quite a few statistical problems on sphere. For example, Head direction tuning 3d direction of object 3d direction of body parts Some 3d tuning There are many standard statistical operations on Euclidean space, like getting mean, standard deviation and generate uniform distribution, fitting a model etc. We can perform these operation without thinking.
Note on CNN Interpretability 2 major way of interpreting CNN Feature visualization: See what a hidden neuron is interested in Attribution: See what part of image activate a filter or detector Activation Atlas These works try to find a tool kits for visualizing DeepNN and building up a human-computer interface of DeepNN.
Based on Goldstein Book Chapter and lecture from Jeff Beck Note on Forms of Memory Definition pin down can be very tricky! Definition Retaining, retrieving, using information after the original information (stimuli) does not present. (Inner view) Any process that some past experience has an effect on the way the subject think and behave in the future. (Outer View) Thus can generalize into even non-animated things! Memory of magnet Use of Memory Longterm Memory Human: Remember things relevant for life. (name, pw, birthday, info about others, address, knowledge) Ecological: cache for food, foraging location. Shorterm Memory Continuity of awareness Different forms Memory has many forms.
Note on Optimization on Manifold Manifold is locally similar to $\R^n$ flat space, but globally not. Manifold is locally homeomorphic to a Euclidean space of the same dimension. Besides there is Riemann logrithm map that connect the local vector space $T_p$ to the neighbourhood of $p$ on the manifold. Thus, many local optimization algorithm that work on flat $\R^n$ space can work the same on neighborhood of a manifold. The unique thing of working on a manifold is how to transport the local direction information in one neighborhood into another neighborhood.
Informative Fragment Approach to Object Recognition It’s intuitive that some basic features in the image of objects are informative to the category of the object. Thus, even for occluded images, the revealed fragments can also provide such information, so that we could recognize the object from few patches.
Note on Feature Visualization Motivation We want to understand what the hidden units “represent” What are they tuned to? What’s the favorite stimuli? Why should we find the most excitable stimuli? Resources DeepDream.ipynb Tensorflow
Deep Unsupervised Learning Lecture notes from Berkeley-cs294 https://sites.google.com/view/berkeley-cs294-158-sp19/home Lec 1 Category Generative Model Non-generative representation learning Motivation for Unsupervised Learning Application Generate/predict fancy samples Detect Anomaly / deviation from distribution Which human can do quite well without training Data Compression (because of predictability) Use the inner representation to do other tasks! Pre-training Type of Question Core Question: Modeling a Distribution
Note on Animal Perception From lecture of Science of Behavior What does it feel to be a bat!? Umwelt: the sensory world of an animal, can be very different from ours. Different precision, range … Use same modality in different ways: Sound Imaging Electro-/Magneto-reception “More extreme your claim, stronger your evidence!”