saliency
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| saliency | docs | |
|---|---|---|
| 4 | 3 | |
| 929 | 6,024 | |
| 0.8% | 0.7% | |
| 3.6 | 9.0 | |
| about 1 month ago | 12 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
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saliency
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[D] Is the math in Integrated gradients (4K citations) wrong?
Found relevant code at https://github.com/PAIR-code/saliency + all code implementations here
- How to display which parts of a single image a Keras model found to be the most significant when making a prediction?
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Gradients of model output layer and intermediate layer wrt inputs
I’m trying to visualize model layer outputs using the saliency core package package on a simple conv net. This requires me to compute the gradients of the model output layer and intermediate convolutional layer output w.r.t the input. I’ve attempted to do this in the last code block, but I run into the error
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A Visual History of Interpretation for Image Recognition
[2]: https://github.com/PAIR-code/saliency
docs
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Anyone willing to help me out on a discord call or something?
This is the model I’m trying to understand and create my own version of: https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/sequences/recurrent_quickdraw.md
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Well technically, C++ *is* Danish...
Seems like a fun task. Tensorflow already has a nice tutorial for image segmentation . You’d have to replace the pets by fruits and then you’d be good.
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Get started with TensorFlow and Deep Learning
TensorFlow docs here
What are some alternatives?
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yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0
tensorflow-deep-learning - All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
darkflow - Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
Deep-Learning-With-TensorFlow-Blog-series - All the resources and hands-on exercises for you to get started with Deep Learning in TensorFlow [Moved to: https://github.com/Rishit-dagli/Deep-Learning-With-TensorFlow]
Emotion_Detection_CNN_keras - Train and test our algorithm using Convolution Neural Networks and classify emotions in real-time.
TensorFlow2.0_Notebooks - Implementation of a series of Neural Network architectures in TensorFow 2.0
vision-camera-realtime-object-detection - VisionCamera Frame Processor Plugin to detect objects using TensorFlow Lite Task Vision
introtodeeplearning - Lab Materials for MIT 6.S191: Introduction to Deep Learning