SlowGuess/NeurIPS_2023_OCR huggingface dataset:
(c) 40 Tasks
ULS	DRPM
T0	92.9±0.6	92.6±0.5
T1	83.4±0.5	83.8±0.5
T2	80.6±0.8	80.6±0.3
T3	82.8±1.1	83.1±0.4
T4	98.6±0.1	98.7±0.1
T5	95.4±0.1	95.4±0.2
T6	81.6±2.2	84.4±1.2
T7	82.7±0.3	82.6±0.2
T8	90.1±0.8	90.7±0.5
T9	94.8±0.2	95.0±0.1
T10	96.0±0.2	95.9±0.1
T11	85.0±0.5	85.0±0.3
T12	91.6±0.6	92.2±0.1
T13	94.8±0.2	94.8±0.2
T14	95.6±0.4	95.7±0.2
T15	99.3±0.1	99.3±0.1
T16	96.2±0.2	96.1±0.1
T17	97.4±0.2	97.5±0.1
T18	91.1±0.1	91.3±0.3
T19	87.1±0.3	87.0±0.5
T20	98.6±0.0	98.5±0.1
T21	93.6±0.1	93.6±0.2
T22	96.0±0.1	95.9±0.2
T23	91.8±0.4	92.6±0.6
T24	95.4±0.2	95.5±0.1
T25	71.5±1.6	72.9±1.2
T26	96.0±0.4	96.4±0.2
T27	74.9±0.5	76.1±0.3
T28	93.5±0.5	94.0±0.3
T29	92.8±0.2	93.5±0.2
T30	96.3±0.3	96.1±0.3
T31	92.7±0.2	93.0±0.1
T32	81.0±0.4	82.1±0.4
T33	83.3±0.2	83.8±0.6
T34	89.2±0.2	89.3±0.1
T35	98.9±0.0	99.0±0.0
T36	91.7±0.1	91.8±0.2
T37	87.6±0.6	88.1±0.1
T38	95.5±0.5	95.6±0.2
T39	87.9±0.1	82.7±3.2
avg(Tasks)	90.6±0.1	90.8±0.1
parameters  $\theta_{1}$  and  $\theta_{2}$  by minimizing the following loss:
$$
\mathcal {L} (X, Y) := \mathcal {L} _ {1} (X, Y) + \alpha \mathcal {L} _ {2} (X, Y)
$$
$$
\mathcal {L} _ {1} (X, Y) := \frac {1}{B} \mathcal {L} _ {C E} (\hat {Y}, Y)
$$
$$
\mathcal {L} _ {2} (X, Y) := \frac {1}{K} \| \boldsymbol {n} (X) - \sum_ {i = 1} ^ {B} \boldsymbol {y} _ {i} \| ^ {2},
$$
where  $\mathcal{L}_{CE}$  denotes the standard cross-entropy loss,  $n$  denotes the output of the MVHG leading to  $\hat{Y}$ , and  $\mathcal{L}_2$  ensures that  $n_i$  matches the number of appearances of label  $i$  in the current batch. Using this simple training scheme, we achieve an f1-score of  $96.43 \pm 0.02$  and  $82.71 \pm 0.03$  on MNIST and FMNIST, respectively, further demonstrating the applicability and versatility of the DRPM to a number of different problems.
Figure 15: Additional partition samples from the DRPM-VC trained on MNIST. The different sets of each partition match each of the digits very well, even after repeatedly sampling from the model.
Figure 16: Additional partition samples from the DRPM-VC trained on FMNIST. Most clusters accurately represent one of the clothing categories and generate new samples very well. The only problem is with the handbag class, where the DRPM-VC learns two different clusters for different kinds of handbags (cluster 5 and 6).
Figure 17: Various samples from each of the generative priors. Each prior learns to represent one of the digits. Further, we see a lot of variation between the different samples, suggesting that the clusters of the DRPM-VC manage to capture some of the diversity present in the dataset.
Sample
Figure 18: Various samples from each of the generative priors. Each prior learns to represent one of the digits. The DRPM-VC learns nice representations that provide coherent generations of most classes. For high-heels (cluster 4), generating new samples seems difficult due to the heterogeneity within that class.