this usage scenario illustrates how the experts can clearly understand the training stage easily
steer the contrastive framework and induce their domain knowledge into the trained model in the
context of data missing with our proposed CIVis system
firstly the expert chooses the data set
and CNN model into the system then he inspects the overall data missing rates in data info to avoid
data missing he ticks 46 features according to his domain knowledge without the features with missing
rate larger than zero following the guidance view the expert moves to the updated negative sampling
view the narrow distribution of green circles and the far distance between the green and red circles
imply that current distribution of embeddings is against the principle of alignment and uniformity
then he wants to refine the distribution by adding samples into the negative collection
therefore he lassos all the semi-data ticks the hard sampling strategy drags the rate slider to
0.6 and clicks apply to automatically add 60% of the selected samples into the negative collection
and he clicks the set negative to confirm his selection in addition the positive sampling
view displays the mapping between the semi-data and their corresponding positive bins which the
expert is satisfied with the default result so he clicks the set positive to confirm this mapping
after specifying the positive and negative samples the expert sets the temperature parameter to 0.07
as recommended in the previous paper (Moco) the model is then trained for short epochs of
100 however the distance variation to the negative collections bar negative rapidly decreases to zero
while the mean distances to both the positive pair and negative collections (mean positive,
mean negative) quickly increase to one these observations suggest that the model collapse
occurs these observations also indicate an overheated training stage to fix this
issue the expert switches to the initialized model and then adjusts the temperature to
one in subsequent training using this setting the expert observes that the mean distances
evolve gradually and the training restores to a normal state after that the training goes with
only semi-data added in the negative collection as a result the decreased mean negative (1 to
0.94) the increased mean positive (0 to 0.97) and The Wider distribution of green dots pose
an evidence that the semi-data are pushed apart as the expert wishes the above results with the
semi-data in the negative collection should be intuitively compared to the case where full data
is added into the negative collection he follows a similar series of operations with above except
that he additionally lassos the full data with a sampling rate of 90% to add 470 samples into the
negative collection therefore it is a comparative study to inspect the influence of adding semi-data
and full data into the negative collection after another round of training the expert
observes that in mean positive the second result is below the first result but in mean negative the
second result goes from the bottom up but is also below the first result in this circle glyph of the
comparison view the distribution of the full data range is wider compared to the first result which
also aligns with experts expectation for the second result the full data are both within the
negative collection and the positive mapping the model must find a balance therefore the second VAR
negative converges slower the embeddings of the full data go closer which means both mean negative
and mean positive increase during the training stage but reach a lower level compared to the
first result after after the training stage the expert has enough confidence in the trained model
with which he clicks infer to apply inference he then validates the prediction by their inference
clicking a row its activation is shown below the highest activation often lies on the grli area
meaning the ground living area in square feet which is important to house price prediction