If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes.
#18 best model for Few-Shot Image Classification on OMNIGLOT - 1-Shot, 20-way
We find that they fail to generalize compositionally and that there is a surprisingly strong negative correlation between compound divergence and accuracy.
We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner.
The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam.
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks.
SOTA for Natural Language Inference on QNLI
We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.
Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not.