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marcellacornia / sam

Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model. IEEE Transactions on Image Processing (2018)
https://ieeexplore.ieee.org/document/8400593
MIT License
205 stars 76 forks source link

coefficient of correlation loss #19

Open ipingtw opened 5 years ago

ipingtw commented 5 years ago

`

def correlation_coefficient(y_true, y_pred):
    max_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(y_pred, axis=2), axis=2)), 
                                                               shape_r_out, axis=-1)), shape_c_out, axis=-1)
    y_pred /= max_y_pred
    sum_y_true = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_true, axis=2), axis=2)), 
                                                               shape_r_out, axis=-1)), shape_c_out, axis=-1)
    sum_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_pred, axis=2), axis=2)), 
                                                               shape_r_out, axis=-1)), shape_c_out, axis=-1)

    y_true /= (sum_y_true + K.epsilon())
    y_pred /= (sum_y_pred + K.epsilon())

    N = shape_r_out * shape_c_out
    sum_prod = K.sum(K.sum(y_true * y_pred, axis=2), axis=2)
    sum_x = K.sum(K.sum(y_true, axis=2), axis=2)
    sum_y = K.sum(K.sum(y_pred, axis=2), axis=2)
    sum_x_square = K.sum(K.sum(K.square(y_true), axis=2), axis=2)
    sum_y_square = K.sum(K.sum(K.square(y_pred), axis=2), axis=2)

    num = sum_prod - ((sum_x * sum_y) / N)
    den = K.sqrt((sum_x_square - K.square(sum_x) / N) * (sum_y_square - K.square(sum_y) / N))

    return -2 * num / den

` Hi thank you for this repo. I am trying to replicate your work but I am having some question understanding your implementation of coefficient of correlation.

Can you please explain why did you divided the prediction by max valuse in the array, and divided both prediction and ground truth by their sum respectively. Is this some kind of optimization trick?