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According to the paper, the query in each meta triplet, there are n data points: T = < x(i), · · · , x(i+n−1) >, x(+), x(−)>
T = < x(i), · · · , x(i+n−1) >, x(+), x(−)>
However, in the code, in the triplet_batch_generator function, I can see that the example/query samples just one datapoint:
triplet_batch_generator
sid = rng.choice(len(inlier_ids), 1, p = positive_weights) examples[i] = inlier_ids[sid]
Have we taken the case n=1 here, or am I missing something?
If this was missed, would love to contribute to fixing this.
The text was updated successfully, but these errors were encountered:
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According to the paper, the query in each meta triplet, there are n data points:
T = < x(i), · · · , x(i+n−1) >, x(+), x(−)>
However, in the code, in the
triplet_batch_generator
function, I can see that the example/query samples just one datapoint:Have we taken the case n=1 here, or am I missing something?
If this was missed, would love to contribute to fixing this.
The text was updated successfully, but these errors were encountered: