Trading robust representations for sample complexity through self-supervised visual experience
Learning in small sample regimes is among the most remarkable features of the human perceptual system. This ability is related to robustness to transformations, which is acquired through visual experience in the form of weak-or self-supervision during development. We explore the idea of allowing artificial systems to learn representations of visual stimuli through weak supervision prior to downstream supervised tasks. We introduce a novel loss function for representation learning using unlabeled image sets and video sequences, and experimentally demonstrate that these representations support one-shot learning and reduce the sample complexity of multiple recognition tasks. We establish the existence of a trade-off between the sizes of weakly supervised, automatically obtained from video sequences, and fully supervised data sets. Our results suggest that equivalence sets other than class labels, which are abundant in unlabeled visual experience, can be used for self-supervised learning of semantically relevant image embeddings.
This paper proposes a framework for representation learning that doesn't require any labeled data. Instead, priors on invariance to spatial transformations or local invariance in video is used to drive representation learning. This paper offers a lot of well thought out formalism and shows some promise on datasets like one-shot MNIST learning. The main downside is that MNIST is particularly suited for such transformations, and even going to a dataset like NORB, this clear edge over competing methods disappears. To me this is an accept because it gives me a lot ideas (and a fleshd out framework for expressing these ideas) and I think this will be true for the community in general.  Strengths. This paper offers a lot of thorough and precise formalism that are general and may find use in a variety of settings. The main idea of defining orbits is interesting. The experiments are thorough and interesting, and the paper is well written.  Weaknesses. It's hard to judge impact in real-world settings when most of the quantitative evaluations are on datasets not representative of complex natural images (e.g. MNIST and NORB). On MNIST, the method shows clear advantages over competing methods. However, even on NORB, where a lot of the deformations can't easily be parameterized, this advantage has turned into being only on par with other leading methods. I think the inclusion of the faces dataset  was important for this reason.  I was confused for a while what the exact orbit was for each dataset. I kept scanning the text for this. A table of all three datasets and a short note on how orbits were defined and canonical samples selected would make things a lot clearer.  Concurrent work. Similar ideas of representation learning through transformation priors have appeared in recent work. I don't think it takes away any novelty from this submission, since judging from the dates these is concurrent works. I just thought I would bring your attention to it: - https://openreview.net/pdf?id=S1v4N2l0- (ICLR 2018) - https://arxiv.org/pdf/1804.01552.pdf (CVPR 2018)  Minor comments. - eq. 6: what connects the orbit with this loss? I don't see the connection just yet - eq. 7: "x_q not in Oxq!=Oxi" What is this notation "set1 != set2" that seems to imply it forms another set (and not a true/false value)  line 136: Oxq \not= Oxi, again, I'm not sure about this notation. I understand what it means, but it looks odd to me. I have never seen this as part of set notation before. - eq. 8: where is x_p, x_q, x_c coming from? Shouldn't the summand be $(x_i, x_p, x_q) \in \mathcal{T}$? The canonical sample x_c is still unclear where it comes from. If x_c is the canonical instance for each orbit, then it also changes in the summation. This is not clear from the notation. - line 196: max unpooling transfers the argmax knowledge of maxpooling to the decoder. Do you use this behavior too? - Table 1: Should EX/NORB really be bold-faced? Is the diff between 0.59+/-0.12 really statistically significant from 0.58+/-0.11? - line 213: are all feature spaces well-suited for 1-NN? If a feature space is not close to a spherical Gaussian, it may perform poorly. If feature dimensions are individually standardized, it would avoid this issue. - It was a bit unclear how canonical samples were constructed on the face dataset ("least yaw displacement from a frontal pose"). This seems to require a lot of priors on faces and does not seem like purely unsupervised learning. Did the other competing methods require canonical examples to be designated?