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Learning from Limited Labeled Data for Visual Recognition
Recent advances in computer vision are in part due to the widespread use of deep neural networks. However, training deep networks require enormous amounts of labeled data which can be a bottleneck. In this thesis, we propose several approaches to mitigate this in the context of modern deep networks and computer vision tasks.
While transfer learning is an effective strategy for natural image tasks where large labeled datasets such as ImageNet are available, it is less effective for distant domains such as medical images and 3D shapes. Chapter 2 focuses on transfer learning from natural image representations to other modalities. In many cases, cross-modal data can be generated using computer graphics techniques. By forcing the agreement of predictions across modalities, we show that the models are more robust to image degradation, such as lower resolution, grayscale, or line drawings instead of color images in high-resolution. Similarly, we show that 3D shape classifiers learned from multi-view images can be transferred to the models of voxel or point cloud representations.
Another line of work has focused on techniques for few-shot learning. In particular, meta-learning approaches explicitly aim to generalize representations by emphasizing transferability to novel tasks. In Chapter 3, we analyze how to improve these techniques by exploiting unlabeled data from related tasks. We show that combining unsupervised objectives with meta-learning objectives can boost the performance of novel tasks. However, we find that small amounts of domain-specific data can be more beneficial than large amounts of generic data.
While transfer learning, unsupervised learning, and few-shot learning have been studied in isolation, in practice, one often finds that transfer learning from large labeled datasets is more effective than others. This is partly due to a lack of evaluation on benchmarks that contains challenges such as class imbalance and domain mismatch. In Chapter 4, we explore the role of expert models in the context of semi-supervised learning on a realistic benchmark. Unlike existing semi-supervised benchmarks, our dataset is designed to expose some of the challenges encountered in a realistic setting, such as the fine-grained similarity between classes, significant class imbalance, and domain mismatch between the labeled and unlabeled data. We show that current semi-supervised methods are negatively affected by out-of-class data, and their performance pales compared to a transfer learning baseline. Last, we leverage the coarse labels from a large collection of images to improve semi-supervised learning. In Chapter 5, we show that incorporating hierarchical labels in the taxonomy improves state-of-the-art semi-supervised methods
Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)
In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field