[HTML][HTML] Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity
Effective identification of tomato plant traits is crucial for timely monitoring and evaluating
their growth and harvest. However, conducting stress experiments on multiple tomato …
their growth and harvest. However, conducting stress experiments on multiple tomato …
Rethinking learning rate tuning in the era of large language models
Large Language Models (LLMs) represent the recent success of deep learning in achieving
remarkable human-like predictive performance. It has become a mainstream strategy to …
remarkable human-like predictive performance. It has become a mainstream strategy to …
On the Efficiency of Privacy Attacks in Federated Learning
Recent studies have revealed severe privacy risks in federated learning represented by
Gradient Leakage Attacks. However existing studies mainly aim at increasing the privacy …
Gradient Leakage Attacks. However existing studies mainly aim at increasing the privacy …
Deep-learning-based hepatic ploidy quantification using H&E histopathology images
Polyploidy, the duplication of the entire genome within a single cell, is a significant
characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy …
characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy …
ps-CALR: Periodic-Shift Cosine Annealing Learning Rate for Deep Neural Networks
There Are Continued Efforts to Build on the Performance of Deep Learning (DL) Models in
Various Fields of Application. Developing New DL Models Continues to Open …
Various Fields of Application. Developing New DL Models Continues to Open …
Guidelines for the regularization of gammas in batch normalization for deep residual networks
L 2 regularization for weights in neural networks is widely used as a standard training trick.
In addition to weights, the use of batch normalization involves an additional trainable …
In addition to weights, the use of batch normalization involves an additional trainable …
Scalable Multimodal Learning and Multimedia Recommendation
J Shen, M Morrison, Z Li - 2023 IEEE 9th International …, 2023 - ieeexplore.ieee.org
Driven by the rapid growth of multimedia big data, multimodal learning (especially
multimodal deep learning) has gained its significant importance and achieved biggest …
multimodal deep learning) has gained its significant importance and achieved biggest …
Primitive Agentic First-Order Optimization
R Sala - arXiv preprint arXiv:2406.04841, 2024 - arxiv.org
Efficient numerical optimization methods can improve performance and reduce the
environmental impact of computing in many applications. This work presents a proof-of …
environmental impact of computing in many applications. This work presents a proof-of …
HyperbolicLR: Epoch insensitive learning rate scheduler
TG Kim - arXiv preprint arXiv:2407.15200, 2024 - arxiv.org
This study proposes two novel learning rate schedulers: the Hyperbolic Learning Rate
Scheduler (HyperbolicLR) and the Exponential Hyperbolic Learning Rate Scheduler …
Scheduler (HyperbolicLR) and the Exponential Hyperbolic Learning Rate Scheduler …
Few-shot Class-agnostic Counting with Occlusion Augmentation and Localization
Most existing few-shot class-agnostic counting (FCAC) methods follow the extract-and-
compare pipeline to count all instances of an arbitrary category in the query image given a …
compare pipeline to count all instances of an arbitrary category in the query image given a …