This is probably the most simple looking and straightforward network ever. Simple yet powerful; wining the ImageNet 2014 Localisation competition and coming second in the Classification track! Problems the Paper Addressed To show that making a network deeper improves its accuracy and also multiple small filters are better than a single large filter when both … Continue reading Paper Explanation: Very Deep Covolutional Networks for Large-Scale Image Recognition (VGGNet)
Category: Deep Learning
Paper Explanation : ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
AlexNet famously won the 2012 ImageNet LSVRC-2012 competition by a large margin (15.3% VS 26.2% (second place) top-5 test error rates). This started the era of deep learning, bringing neural networks back into the spotlight! Problems the paper addressed To show that it is possible to successfully train a deep CNN with a large number … Continue reading Paper Explanation : ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
Paper Explanation: Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks
What Led to Faster R-CNN? Even though networks like Fast R-CNN achieve a really high accuracy, they are very slow to be of practical use in real time. The reason for such slow speeds is the region proposal step in the architecture! Algorithms, like Selective Search (used in Fast R-CNN) take around 2 seconds per … Continue reading Paper Explanation: Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks
Paper Explanation: Fast R-CNN
Called Fast R-CNN because it's comparatively fast to train and test. Why Fast R-CNN? Even though R-CNN had achieved state of the art performance on object detection it had many problems: Slow at test-time due to independent forward passes of the CNN for each region proposal. The CNN doesn't get trained during the training of the … Continue reading Paper Explanation: Fast R-CNN
What are Generative Learning Algorithms?
I will try to make this post as light on mathematics as is possible, but a complete in depth understanding can only come from understanding the underlying mathematics! Generative learning algorithms are really beautiful! Like anything they have there advantages and disadvantages. These algorithms are a bit "tough" to understand but comparing them by the … Continue reading What are Generative Learning Algorithms?
My thoughts on the Deep Learning Specialization on Coursera by deeplearning.ai
Having just finished the specialization, I want to share my thoughts on how I felt about the whole journey. Before I start, I want to mention my experience and knowledge in deep learning prior to taking the specialization. I had watched the lecture videos of the Stanford Computer Vision and deep learning course, CS … Continue reading My thoughts on the Deep Learning Specialization on Coursera by deeplearning.ai