Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives (2003.10739v2)

Abstract: While the depth of modern Convolutional Neural Networks (CNNs) surpasses that of the pioneering networks with a significant margin, the traditional way of appending supervision only over the final classifier and progressively propagating gradient flow upstream remains the training mainstay. Seminal Deeply-Supervised Networks (DSN) were proposed to alleviate the difficulty of optimization arising from gradient flow through a long chain. However, it is still vulnerable to issues including interference to the hierarchical representation generation process and inconsistent optimization objectives, as illustrated theoretically and empirically in this paper. Complementary to previous training strategies, we propose Dynamic Hierarchical Mimicking, a generic feature learning mechanism, to advance CNN training with enhanced generalization ability. Partially inspired by DSN, we fork delicately designed side branches from the intermediate layers of a given neural network. Each branch can emerge from certain locations of the main branch dynamically, which not only retains representation rooted in the backbone network but also generates more diverse representations along its own pathway. We go one step further to promote multi-level interactions among different branches through an optimization formula with probabilistic prediction matching losses, thus guaranteeing a more robust optimization process and better representation ability. Experiments on both category and instance recognition tasks demonstrate the substantial improvements of our proposed method over its corresponding counterparts using diverse state-of-the-art CNN architectures. Code and models are publicly available at https://github.com/d-li14/DHM

• Challenges of Unavailable Academic Papers on arXiv involve instances where listed research papers lack accessible content like abstracts or PDFs, hindering scholarly engagement.
• The absence of accessible manuscripts severely impacts comprehensive literature reviews, making it difficult for researchers to build upon existing knowledge effectively.
• Non-availability directly threatens research reproducibility and complicates data sharing, highlighting the need for robust academic infrastructures and submission standards.

Analysis of the Unavailable Paper on cs.CV

The present examination concerns an entry on arXiv under the computer vision category, specifically with the identifier (Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives, 2020)v2. However, it is noteworthy that neither the paper's title, author information, nor the abstract is accessible. Furthermore, there is no available PDF download option, a missing component that further impedes thorough analysis. Despite the lack of content, a discussion can still be structured around the implications and challenges presented by such situations in academic dissemination.

Implications on Research Accessibility

The absence of accessible document components in academic repositories raises significant considerations within the research community. While repositories like arXiv aim to facilitate open access to academic work, cases where papers are not fully accessible highlight a gap in the dissemination process. This can be particularly challenging for researchers seeking to leverage existing work to advance their own studies, as key insights and data may remain out of reach.

1. Impact on Literature Reviews:
   • The absence of a publicly available paper can inhibit comprehensive literature reviews. Researchers aiming to create an aggregated understanding of the current state of the art may find their analyses incomplete or requiring additional effort to source the missing information.
2. Data Sharing and Reproducibility:
   • The non-availability of manuscripts threatens reproducibility, a cornerstone of scientific inquiry. Without access to methodologies, datasets and results, independent verification, and application of findings in real-world scenarios, are hindered.

Considerations for Academic Infrastructure

The situation serves as a reminder of the importance of robust academic infrastructures that ensure the availability of research outputs. Platforms hosting academic papers should prioritize full document availability to guarantee that the scholarly community can engage with all contributions equally.

• Technical Solutions: Implementing more stringent requirements for document submissions, such as mandatory PDF uploads, could be a practical step for maintaining the integrity and accessibility of academic resources.
• Community Engagement: Encouraging authors to provide all necessary components of their work fosters a more collaborative environment. Educational initiatives that emphasize the importance of full access to research may aid in mitigating incomplete submissions.

Looking Forward

Moving forward, it is prudent for academic communities and repository managers to consider how incomplete accessibility affects research growth and scholarly engagement. While it is likely that technological or infrastructural hurdles occasionally cause such issues, addressing them is essential to fostering an inclusive and integrated landscape for knowledge advancement.

In summary, while a direct analysis of the content of (Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives, 2020)v2 is unfeasible due to unavailable materials, this absence itself prompts valuable discourse on the necessities and responsibilities inherent in academic publishing and knowledge sharing.