You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Although our method can achieve compelling results in many cases, the results are far from uniformly positive. Figure 17 shows several typical failure cases. On translation tasks that involve color and texture changes, as many of those reported above, the method often succeeds. We have also explored tasks that require geometric changes, with little success. For example, on the task of dog\rightarrowcat transfiguration, the learned translation degenerates into making minimal changes to the input (Figure 17). This failure might be caused by our generator architectures which are tailored for good performance on the appearance changes. Handling more varied and extreme transformations, especially geometric changes, is an important problem for future work. Some failure cases are caused by the distribution characteristics of the training datasets. For example, our method has got confused in the horse \rightarrow zebra example (Figure 17, right), because our model was trained on the wild horse and zebra synsets of ImageNet, which does not contain images of a person riding a horse or zebra. [We also observe a lingering gap between the results achievable with paired training data and those achieved by our unpaired method. In some cases, this gap may be very hard – or even impossible – to close: for example, our method sometimes permutes the labels for tree and building in the output of the photos→labels task. Resolving this ambiguity may require some form of weak semantic supervision. Integrating weak or semi-supervised data may lead to substantially more powerful translators, still at a fraction of the annotation cost of the fully-supervised systems.]

Question: Why is this method unable to achieve compelling results with any of the baselines in Figure 5, 6?
[The paper’s generator architectures which are tailored for good performance on the appearance changes might cause failures. For example, on the task of dog→cat transfiguration, the learned translation degenerates into making minimal changes to the input. Some failure cases are caused by the distribution characteristics of the training datasets. For example, the paper’s method has got confused in the horse→zebra example, because the paper’s model was trained on the wild horse and zebra synsets of ImageNet, which does not contain images of a person riding a horse or zebra. The paper also observes a lingering gap between the results achievable with paired training data and those achieved by the paper’s unpaired method. In some cases, this gap may be hard or even impossible to close: for example, the paper’s method sometimes permutes the labels for tree and building in the output of the photos→labels task. Resolving this ambiguity may require some form of weak semantic supervision. Integrating weak or semi-supervised data may lead to substantially more powerful translators, still at a fraction of the annotation cost of the fully-supervised systems.]