While training a deep network on multiple tasks jointly is easy, training on multiple tasks sequentially is challenging. This challenge is addressed in various literature: Lifelong Learning, Incremental Learning, Continual Learning, and Never-Ending Learning. Within these forms, the common problem is catastrophic forgetting, i.e., the network forgets older tasks. To…

Deep learning stands on two pillars: GPUs and large datasets. Thus, deep networks suffer when trained from scratch on small datasets. This phenomenon is known as overfitting. This paper [1] proposes knowledge evolution to reduces both overfitting and the burden for data collection. In the paper, knowledge evolution is supported…

Every software engineer has used a debugger to debug his code. Yet, a neural network debugger… That’s news! This paper [1] proposes a debugger to debug and visualize attention in convolutional neural networks (CNNs).

Before describing the CNN debugger, I want to highlight few attributes of program debuggers (e.g., gdb)…

Thanks, Kubra, for sharing your thoughts. I never used Deep image prior. It is a cool idea, yet, its training cost is a barrier. As you mentioned, the total time spent on training is optimized; that's correct. Yet, this training time is incurred for every image, i.e., every image is a training sample, there is no inference.

Furthermore, I had a colleague who used deep prior for RGB-D images [1]. The training cost becomes more severe as the number of dimensions increases.

So, Yes, for 2D images the cost might be manageable, but I would proceed with caution for higher dimensions (e.g., videos). Thanks again for sharing your input :)

[1] Depth Completion Using a View-constrained Deep Prior

Metric learning learns a feature embedding that quantifies the similarity between objects and enables retrieval. Metric learning losses can be categorized into two classes: pair-based and proxy-based. The next figure highlights the difference between the two classes. Pair-based losses pull similar samples together while pushing different samples apart (data-to-data relations)…

Ahmed Taha

I write reviews on computer vision papers. Writing tips are welcomed.

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