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{
"input": "Explainer: hello, you can ask me any questions to help you better understand the explanation on the left screen. Seeker: hello, does the LIME model depend on the size of the object of the image analyzed or it is just going to pick up distinct features of the object? Explainer: LIME will segments the image into a set of superpixels (i.e., contiguous pixels that are similar to each other) Explainer: Then it will find the top 5 super-pixels that are important to the model prediction. Seeker: ok can. may i ask if the LIME model is better than the other models? and how are they similar? Explainer: There are different explanation methods, e.g., Grad-cam or Integrated gradients. They can all explain model's prediction. But it's hard to universally say one model will be better than others. Seeker: What is the classification model and what is the explanation model?"
} | {
"output": "Explainer: The classification model classify the visual input into a class. For the example on the left screen, the classification model classify the input image into the leopard class. and the explanation model (LIME) tries to explain how the classification model classifies the visual input into a specific class by generating the second image on the left screen."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: How does the LIME model sample data points/regions when interpreting an input image"
} | {
"output": "Explainer: It will first group the original image into super-pixels. A superpixel is a group of connected pixels in an image that share a similar color. we can perturb the pixel value of superpixel to get sampled data points."
} |
{
"input": "Explainer: hello, you can ask me any questions to help you better understand the explanation on the left screen. Seeker: hello, does the LIME model depend on the size of the object of the image analyzed or it is just going to pick up distinct features of the object? Explainer: LIME will segments the image into a set of superpixels (i.e., contiguous pixels that are similar to each other) Explainer: Then it will find the top 5 super-pixels that are important to the model prediction. Seeker: ok can. may i ask if the LIME model is better than the other models? and how are they similar? Explainer: There are different explanation methods, e.g., Grad-cam or Integrated gradients. They can all explain model's prediction. But it's hard to universally say one model will be better than others. Seeker: What is the classification model and what is the explanation model? Explainer: The classification model classify the visual input into a class. Explainer: For the example on the left screen, the classification model classify the input image into the leopard class. Explainer: and the explanation model (LIME) tries to explain how the classification model classifies the visual input into a specific class by generating the second image on the left screen. Seeker: Okay. I see. Why do the yellow edges in the explanation image appear jagged"
} | {
"output": "Explainer: That's because the image is first grouped into super-pixels. It is based on the similarity between closed pixels. So it may be not very smooth."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Hi, is it possible to generate the heatmap based on only a photo?"
} | {
"output": "Explainer: The heatmap is generated based on the importance of image regions to the classification model. The classification model is a deep learning model that can classify an image into a category. It can classify the first image into the goldfish category. But the classification model is very complex to understand for humans. Explanation models, like Grad-cam we used here, are used to explain how the classification model makes such a prediction.\nIt generates explanations by highlighting regions of an image that are most important for the classification model's prediction. This is the heatmap shown in the second image."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: How does the LIME model sample data points/regions when interpreting an input image Explainer: It will first group the original image into super-pixels. Explainer: A superpixel is a group of connected pixels in an image that share a similar color. Explainer: we can perturb the pixel value of superpixel to get sampled data points. Seeker: I see, thanks that is very clear. Seeker: Once the model selects super pixels, how does it prioritise which super pixels to accept or reject when making predictions of the more complex model? How many rounds of decisions will it make? Explainer: LIME works by training a local surrogate model around a specific prediction. The surrogate model is a white box model and inherently interpretable. Explainer: We will use sampled data points to train the surrogate model. Explainer: An example surrogate model is the linear model. Explainer: The output is the sum of input features with weights. The weights be used to indicate the importance of input features, i.e., the super-pixels. Explainer: we output the top 5 super-pixels that are important to the model prediction. Explainer: That's how we get the second image. Seeker: ok, I think I understand!"
} | {
"output": "Explainer: Do you have other questions?"
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: Hi, is it possible to generate the heatmap based on only a photo? Explainer: The heatmap is generated based on the importance of image regions to the classification model. Explainer: The classification model is a deep learning model that can classify an image into a category. It can classify the first image into the goldfish category. But the classification model is very complex to understand for humans. Explainer: Explanation models, like Grad-cam we used here, are used to explain how the classification model makes such a prediction.\nIt generates explanations by highlighting regions of an image that are most important for the classification model's prediction. This is the heatmap shown in the second image. Seeker: I see. So a heat sensor is not required? A heatmap is automatically generated from each photo and analysed using the model."
} | {
"output": "Explainer: Yes. exactly. There is no heat sensor. The second image is just shown in a heatmap way to illustrate which regions are important to the classification model."
} |
{
"input": "Explainer: hello, you can ask me any questions to help you better understand the explanation on the left screen. Seeker: hello, does the LIME model depend on the size of the object of the image analyzed or it is just going to pick up distinct features of the object? Explainer: LIME will segments the image into a set of superpixels (i.e., contiguous pixels that are similar to each other) Explainer: Then it will find the top 5 super-pixels that are important to the model prediction. Seeker: ok can. may i ask if the LIME model is better than the other models? and how are they similar?"
} | {
"output": "Explainer: There are different explanation methods, e.g., Grad-cam or Integrated gradients. They can all explain model's prediction. But it's hard to universally say one model will be better than others."
} |
{
"input": "Explainer: hello, you can ask me any questions to help you better understand the explanation on the left screen. Seeker: hello, does the LIME model depend on the size of the object of the image analyzed or it is just going to pick up distinct features of the object? Explainer: LIME will segments the image into a set of superpixels (i.e., contiguous pixels that are similar to each other) Explainer: Then it will find the top 5 super-pixels that are important to the model prediction. Seeker: ok can. may i ask if the LIME model is better than the other models? and how are they similar? Explainer: There are different explanation methods, e.g., Grad-cam or Integrated gradients. They can all explain model's prediction. But it's hard to universally say one model will be better than others. Seeker: What is the classification model and what is the explanation model? Explainer: The classification model classify the visual input into a class. Explainer: For the example on the left screen, the classification model classify the input image into the leopard class. Explainer: and the explanation model (LIME) tries to explain how the classification model classifies the visual input into a specific class by generating the second image on the left screen. Seeker: Okay. I see. Why do the yellow edges in the explanation image appear jagged Explainer: That's because the image is first grouped into super-pixels. It is based on the similarity between closed pixels. So it may be not very smooth. Seeker: I don't have any more questions."
} | {
"output": "Explainer: okay. Thanks for your questions."
} |
{
"input": "Explainer: hello, you can ask any questions to help you better understand the explanation on the left screen. Seeker: How does the LIME model sample data points/regions when interpreting an input image Explainer: It will first group the original image into super-pixels. Explainer: A superpixel is a group of connected pixels in an image that share a similar color. Explainer: we can perturb the pixel value of superpixel to get sampled data points. Seeker: I see, thanks that is very clear. Seeker: Once the model selects super pixels, how does it prioritise which super pixels to accept or reject when making predictions of the more complex model? How many rounds of decisions will it make?"
} | {
"output": "Explainer: LIME works by training a local surrogate model around a specific prediction. The surrogate model is a white box model and inherently interpretable. We will use sampled data points to train the surrogate model. An example surrogate model is the linear model. The output is the sum of input features with weights. The weights be used to indicate the importance of input features, i.e., the super-pixels. we output the top 5 super-pixels that are important to the model prediction. That's how we get the second image."
} |