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- https://youtu.be/Mljfam5zNJo?t=66 - Theme: Architecture
- Densely Connected Convolutional Networks
- https://arxiv.org/abs/1608.06993
- Best paper award (Facebook)
- tl;dr; Connect the conv layer not only with the next layer but also with some layers after it.
- https://youtu.be/Mljfam5zNJo?t=484
- Annotating Object Instances with a Polygon-RNN
- https://arxiv.org/abs/1704.05548
- tl;dr; conv LSTM that predicts a sequence of points (possibly starting with a user provided point)
- https://youtu.be/Mljfam5zNJo?t=1036 - Theme: Vision tasks
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- https://arxiv.org/abs/1612.00593
- tl;dr; [classify/segment point cloud, need to be able to rearange or rotate points]
- use operations that either ignore permutations in input or permute out in a same way
- https://youtu.be/Mljfam5zNJo?t=1494
- FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence
- https://arxiv.org/abs/1702.00926
- tl;dr; [deep learned sift replacement] no arch review
- https://youtu.be/Mljfam5zNJo?t=1762
- Full Resolution Image Compression with Recurrent Neural Networks
- https://arxiv.org/abs/1608.05148
- no arch review
- https://youtu.be/Mljfam5zNJo?t=1852
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
- https://arxiv.org/abs/1609.04802
- (Twitter)
- no arch review
- https://youtu.be/Mljfam5zNJo?t=1997
- One-to-Many Network for Visually Pleasing Compression Artifacts Reduction
- https://arxiv.org/abs/1611.04994
- tl;dr; [recover image after JPEG compression] add an aditional loss that recovered image should match input when compressed via JPEG
- https://youtu.be/Mljfam5zNJo?t=2307
- Network Dissection: Quantifying Interpretability of Deep Visual Representations
- https://arxiv.org/abs/1704.05796
- tl;dr; [interpret how the network sees]
- a) build a heatmap of pixels that activate some neuron in the middle of the network
- b) compare this heatmap with the ground truth for the segmentation task.
- c) if the heatmap and the GT match for a specific class then mark this neuron as the one that activates for this class
- Apparently there are quite a lot of neurons like this
- https://youtu.be/Mljfam5zNJo?t=2584
- Training Sparse Neural Networks
- https://arxiv.org/abs/1611.06694
- Local Binary Convolutional Neural Networks
- https://arxiv.org/abs/1608.06049
- https://youtu.be/Mljfam5zNJo?t=2655
- Universal Adversarial Perturbations
- https://arxiv.org/abs/1610.08401
- tl;dr; [find a universal image (~noise) that you can add to any image and it will lead to misclassification]
- https://youtu.be/Mljfam5zNJo?t=2759 - Wrap up, questions
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