Md Foysal Ahmed

Yes, a new deep learning model with lower accuracy than the existing model can still be considered for publication in academic journals or conferences. The key is to provide a clear rationale for the significance of your proposed model.

For example, a model that offers a novel approach or theoretical insight can pave the way for future research and development. Similarly, a model that is less complex and more computationally efficient may be more applicable in real-world scenarios where resources are limited. Moreover, aspects such as interpretability, robustness against data shifts or adversarial attacks, and domain-specific performance are also critical. A model that excels in these areas can be more beneficial than one with marginally higher accuracy. It is crucial to clearly communicate the significance of these contributions in the research manuscript, highlighting how the model advances knowledge, opens new perspectives, or addresses specific challenges in the field.

Dear Md Foysal Ahmed ,

Your situation presents a common challenge in research, model designs, and development. While it's natural to strive for the highest possible accuracy, several factors must be considered beyond the numerical performance metric. In fact, accuracy is a single performance evaluation that you may even consider not reporting depending on the aim and objectives of your research.

Look, achieving a 90% accuracy is still commendable, particularly if your model addresses a novel problem or introduces innovative approaches. Remember, the significance of your work isn't solely determined by the numerical accuracy but also by its contribution to advancing knowledge in your field. If your model introduces a new perspective or methodology or tackles a previously unsolved problem, it can still be valuable and worthy of publication. So, now worries. In your manuscript, focus on articulating the unique contributions of your model. Emphasize aspects such as its interpretability, scalability, efficiency, or robustness, which may compensate for the slightly lower accuracy compared to existing models. Highlight any insights gained from your model, such as identifying patterns or relationships that weren't apparent before.

When comparing your model to existing ones, be transparent about its performance relative to the benchmarks. Acknowledge the higher accuracy achieved by some existing models, but also discuss the trade-offs and limitations associated with them. For instance, do they require extensive computational resources, specialized data preprocessing, or domain-specific knowledge? If your model offers advantages in terms of simplicity, generalizability, or ease of implementation, highlight these aspects to provide a balanced perspective. Additionally, consider conducting a thorough analysis of error patterns to gain deeper insights into your model's performance. Understanding where and why your model fails can lead to valuable insights for future improvements or research directions.

I think, don't underestimate the importance of peer feedback and validation. Submit your work to reputable conference or journal in your field, where it can undergo rigorous review by experts. Even if your model's accuracy is slightly lower than existing benchmarks, if your research methodology is sound and your contributions are significant, it can still make a meaningful impact and contribute to the advancement of knowledge in your domain in the future.

Success is waiting for your discussion insights in your paper.

I hope it helps,

Shafik

Md Foysal Ahmed

Wasswa Shafik

Hussain Nizam

A new deep learning model with lower accuracy than an existing model can still be publishable if it offers other significant contributions to the field. Factors such as improved interpretability, reduced computational complexity, robustness to adversarial attacks, better performance on specific sub-tasks or under particular conditions, or novel methodologies and architectures can make the research valuable. Additionally, insights gained from why the model underperforms compared to existing models can be beneficial for the scientific community. As long as the research provides meaningful advancements or understanding in deep learning, it can warrant publication even if the accuracy is not superior.

Md Foysal Ahmed Wasswa Shafik

Hussain Nizam

A new deep learning model with an accuracy lower than the existing model can still be publishable if it offers other significant contributions to the field. Factors such as novel architecture, improved computational efficiency, interpretability, robustness to specific types of data, or addressing previously unsolved problems can make the model valuable. The research might also provide insights into the limitations and challenges of current models, offer theoretical advancements, or propose innovative techniques that could be beneficial for future developments. Therefore, while accuracy is an important metric, the overall impact, originality, and potential applications of the research can justify its publication.

It's great you're aiming to publish your work! Here's a breakdown of your situation and how to handle it:

Is it Publishable?

• Yes, potentially! While your model's accuracy is lower than some existing ones, there's still value in your work. Focus on the novelty and contributions you bring to the field.
• Factors for Publishability: Novelty: Did you use a unique approach or dataset? Contributions: Did your model improve other aspects like speed, efficiency, or interpretability? Strong Analysis: Did you comprehensively analyze the performance of your model and provide insights into its strengths and weaknesses? Context: What are the limitations of the existing models your work compares to? Potential for Future Work: Does your work open doors for further research and improvement?

How to Handle Comparison and Recent Work:

• Be Transparent: Clearly present the accuracy of your model compared to existing models.
• Focus on Strengths: Don't just dwell on the accuracy gap. Highlight the unique aspects of your model, its advantages, and how it complements or surpasses the existing models in certain areas.
• Discuss Limitations: Be honest about the limitations of your model, but frame them as opportunities for future research.
• Highlight Improvements: If your model outperforms in other areas, clearly demonstrate those advantages.
• Contextualize: Explain the limitations of the existing models and the reasons for their higher accuracy. This can help justify your model's performance within its specific context.
• Future Directions: Propose avenues for further research to improve the accuracy of your model.

Remember: Publications are not only about achieving the absolute best accuracy. They are about making contributions to the field, even if those contributions are incremental.

Example: Imagine your model is significantly faster and more efficient than existing models, even though it has slightly lower accuracy. This could be a significant contribution, especially if the speed or efficiency advantage is crucial for a particular application.

Key Takeaway:

• Focus on the unique aspects of your work, its contributions, and its potential for future development. A well-written and comprehensive paper highlighting these aspects can still be impactful and publishable.