Sampling Bias in Deep Active Classification: An Empirical Study
Table 14: Intersection across query strategies using 19 and 9 iterations (mean ± std across runs) and different seeds
['Dsets SGN', 'Chance 9.4±0.0', 'FTZ Ent-Ent 81.6±0.1', 'FTZ Ent-LC 80.1±0.3', 'MNB Ent-Ent 52.9±0.0', 'MNB Ent-LC 39.8±0.0', 'FTZ Ent-Ent 74.8±0.3', 'FTZ Ent-LC 73.4±0.6', 'MNB Ent-Ent 35.0±0.0', 'MNB Ent-LC 34.1±0.0']
Acquisition Functions: We compare four uncertainty query strategies: Least Confidence (LC) and Entropy (Ent), with and without deletion of least uncertain samples from the training set. Deletion of least uncertain samples reduces the dependence on the initial randomly selected set. We present five of the ten possible combinations and again observe the high degree of overlap in the collected samples. It can be concluded that the approach is fairly robust to these variations in the query strategy.