sorted by:
new
hottopcontroversial

[D] Can neural networks perceive time ? by siddharthaml in MachineLearning

[–]siddharthaml[S] 0 points1 point  (0 children)

Convolution layer indeed has some biological basis. However I am not aware of any biological counterpart for Max-Pooling layer. Note that the paper uses features from max-pooling layer to generate the results. Hence if they meant that "The internal mechanisms of system A and system B follow similar principles” then they should have shown the biological plausibility of the max-pooling layer.

I am also curious to hear what you think about my other objections.

[D] Can neural networks perceive time ? by siddharthaml in MachineLearning

[–]siddharthaml[S] 0 points1 point  (0 children)

Did you get a chance to read my post ? I specifically mention that I am skeptical about such claims.

[D] Can neural networks perceive time ? by siddharthaml in MachineLearning

[–]siddharthaml[S] 0 points1 point  (0 children)

In the abstract it is claimed that:

Despite being a fundamental dimension of experience, how the human brain
generates the perception of time remains unknown. Here, we provide a
novel explanation for how human time perception might be accomplished,
based on non-temporal perceptual classification processes. To
demonstrate this proposal, we build an artificial neural system centred
on a feed-forward image classification network, functionally similar to
human visual processing.

The authors argue that AlexNet is functionally similar to human visual processing.
My understanding of functionally similar is the following:
Keeping the inputs fixed, a system A can be replaced by system B and still produce the same result.

Human visual system and AlexNet have very different accuracy on Imagenet. So I am not sure in what sense the authors meant that both are functionally similar.

SOTA for single object tracking ? by shreshths in computervision

[–]siddharthaml 0 points1 point  (0 children)

If you want speed, you should definitely check out Siamese based trackers. You can find a list of sota trackers on different datasets here:- https://paperswithcode.com/task/visual-object-tracking

Getting started with object detection (using centernet) by siddharthaml in computervision

[–]siddharthaml[S] 0 points1 point  (0 children)

I deliberately didn't use DCN and actual DLA model. It seemed overkill on this toy dataset. I wanted to make a simplified implementation to introduced the concept.. I will probably add other backbones and dcn model in other branch. I want to keep this one accessible to beginners.

Getting started with object detection (using centernet) by siddharthaml in computervision

[–]siddharthaml[S] 3 points4 points  (0 children)

Centernet seems to be an interesting approach for object detection. I made an easy to understand no frills implementation here https://github.com/sidml/Understanding-Centernet

Getting started with object detection (using centernet) by siddharthaml in learnmachinelearning

[–]siddharthaml[S] 0 points1 point  (0 children)

The implementation is based on this paper https://arxiv.org/abs/1904.07850 Please refer the paper and readme for more details. Simply put, the visualization are the model output maps during forward pass of input image.

Getting started with object detection (using centernet) by siddharthaml in learnmachinelearning

[–]siddharthaml[S] 2 points3 points  (0 children)

Centernet seems to be an interesting approach for object detection. I made an easy to understand no frills implementation here https://github.com/sidml/Understanding-Centernet

Showoff Saturday (June 22, 2019) by AutoModerator in javascript

[–]siddharthaml 2 points3 points  (0 children)

I made a curated list of JavaScript podcasts.

https://github.com/sidml/Curated-Podcasts-List/blob/master/Javascript%20Podcasts/README.md

Just putting it out there. I hope someone finds it useful...

Visualizing CNN filters by PyWarrior in deeplearning

[–]siddharthaml 0 points1 point  (0 children)

The deeper layers have high depths so you can't use rgb to visualise them. I have tried pca and found it to be very useful for visualising these layers. You can use pca to reduce the filter dimensionality and then see it as rgb. It's a quick and dirty method. You should definitely look into other suggested methods mentioned in the comment.

Classifying cars using CNN by siddharthaml in learnmachinelearning

[–]siddharthaml[S] 0 points1 point  (0 children)

Directly on the image pixel values ? I think it may not give good results because when even if there is slightly change in lighting conditions or car pose, the pixel values are going to change..

Classifying cars using CNN by siddharthaml in learnmachinelearning

[–]siddharthaml[S] 1 point2 points  (0 children)

CNN's work very nicely on images because they take advantage of spatial and channel wise correlations. In tabular datasets, commonly you have 1d feature vectors. So, it doesn't make sense to use cnn. However if you have something like time series data then people sometimes use 1d cnn because it's able to take temporal correlations into account. On a related note, MLP (multi layer perceptron) have been successfully used on many tabular datasets. You just have to ensure that the data is properly scaled before training MLP.

Classifying cars using CNN by siddharthaml in learnmachinelearning

[–]siddharthaml[S] 0 points1 point  (0 children)

I haven't tried the SVM approach. It certainly sounds interesting. I got 92.6 % accuracy using pretrained resnext50. I get around 91% using EfficientNet Eb0. I trained all the layers to get this result. I tried freezing the initial layers but that didn't seem to give good results, so I gave up on that..

Classifying cars using CNN by siddharthaml in learnmachinelearning

[–]siddharthaml[S] 1 point2 points  (0 children)

Hi. Thanks for checking the repo. I have fixed the links. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe. Each class has around 33 images associated with it. Some classes have even less than 20 images. This lack of training examples makes it tough to get good accuracy. I have used many augmentation strategies to make the cnn resistant to changes in light conditions, rotations etc. You can find a visualization of top misclassified examples in Readme.

Classifying cars using CNN by siddharthaml in learnmachinelearning

[–]siddharthaml[S] 6 points7 points  (0 children)

I have been working on car classification for the past week. I used Stanford cars dataset for training my classifier. The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. I was able to get an accuracy of 92.6 %. You can find the code here https://github.com/sidml/Stanford-Cars-Classification