We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models.

Humans can quickly learn new tasks from just a handful of examples by preserving rich representations of experience [1].

While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling.

We've provided a webpage for our dataset, which contains a link to the dataset. Our dataset is currently available through our webpage (and directly via this link). We use a learning rate of 0.01 for ResNet-18 and0.1forResNet-50. This loss is motivated by recent contrastive learning to maximize the probability for the neural network to select the corresponding patch in both the original imagexI and the generated image ˆxI. For reference, we also show the image that corresponds to the tactile example at rightmost (not used by the model).