Cutting-edge Machine Learning Models Across Various Domains
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Chapter 1: Introduction to State-of-the-Art Models
The landscape of machine learning is ever-evolving, with state-of-the-art (SOTA) models constantly emerging. Having participated in numerous Kaggle competitions over the past year, I have encountered a variety of these models, undertaking evaluations and comparisons. Thus, I thought it would be beneficial to compile a list of leading models for various machine learning tasks, providing a starting point for your own explorations.
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Now, let's get started!
Section 1.1: Image Classification
EfficientNetV2
EfficientNetV2 has outperformed existing image classification networks by 2%, all while training 5 to 11 times faster. This is a significant improvement, as training speed has long been a challenge in machine learning, often complicating the debugging of network issues. Though these models have yet to undergo extensive testing due to their recent release, it’s safe to say that EfficientNet remains a leading model in this domain.
Normalizer-Free Networks (NF-Nets)
This year’s models have shifted their focus from merely enhancing performance on benchmark datasets like ImageNet21k to significantly improving training speeds. NF-Nets eliminate batch normalization and introduce Adaptive Gradient Clipping, achieving a remarkable 9-fold increase in training speed while maintaining SOTA performance on ImageNet.
Section 1.2: Image Segmentation
Efficient U-Nets
U-Nets have proven effective in image segmentation tasks. A recent trend has involved optimizing U-Nets by integrating SOTA CNNs as the U-Net encoder or backbone. Notably, a recent study has shown impressive results by replacing the U-Net encoder with EfficientNet, which has gained traction in various image segmentation competitions.
Section 1.3: Object Detection
YoloV5
Yolo has established itself as a prominent player in the object detection realm, requiring no further introduction given the plethora of resources available.
VarifocalNet (VF-Net)
During an object detection competition, I encountered VF-Net and was impressed by its performance. It often matched or even surpassed Yolo’s results. The creators of VF-Net introduced an innovative "IoU-aware Classification Score" that effectively filters predicted boxes for enhanced Average Precision. Additionally, they developed a new loss function named Varifocal loss and proposed a novel "star-shaped bounding box," which I find quite intriguing. I'm planning to write a dedicated article on this topic soon.
Section 1.4: Tabular Data & Time-Series
Tabular data remains a crucial area in machine learning, as many projects are built on this format. Surprisingly, classical machine learning algorithms often outperform deep learning networks in this context.
Gradient Boosting Machines (LGBM, XGBoost & Catboost)
I was astonished to see a straightforward algorithm like Light-GBM outperforming more complex neural networks in a Kaggle competition. While the performance gap was not monumental, achieving even a 1-2% improvement in the 90s is quite challenging. Gradient boosting machines have been around for a significant time, built on decision trees. Their interpretability and user-friendliness make them an excellent starting point for beginners in machine learning.
Graph Neural Networks (GNNs)
GNNs have garnered considerable attention in the machine learning field. Their flexibility allows for transforming many text-based problems into graph-based challenges, making GNNs applicable across various domains. Moreover, GNNs can be employed in image analysis via Graph Convolutional Networks. The integration of Attention mechanisms with GNNs has led to numerous innovative solutions.
Chapter 2: Natural Language Processing
In the realm of Natural Language Processing (NLP), transformers have become the benchmark models. Rather than revisiting the fundamentals of transformers, let’s explore some of the latest transformer-based innovations.
OpenAI GPT-3
GPT-3 stands out as one of the most formidable models in NLP today. Numerous articles highlight its ability to generate code, create content, and answer inquiries. However, I remain skeptical about its potential to replace human writers and coders, as it primarily generates outputs based on pre-existing online content.
BERT and Google Switch Transformers
While not definitively SOTA, Google Switch Transformers rank among the top NLP models, especially as they "scale to trillion parameters."
Chapter 3: Unsupervised and Reinforcement Learning
Unsupervised learning deserves more recognition than it typically receives. I've personally found immense satisfaction in working on unsupervised machine learning projects. Although there are effective classical unsupervised algorithms, I will focus on deep learning in line with this article's tone.
- Variational Autoencoders (VQ-VAE)
Variational Autoencoders enhance the classic encoder-decoder framework. Their bottlenecks are divided into two layers—"mean" and "std-dev"—which feed into a sampling layer designed to represent dataset distribution. This allows for generating new data points based on the learned distribution.
- Reinforcement Learning
I may not be an expert in reinforcement learning, but I can share valuable resources for those interested in delving deeper.
- Deep Q-networks
- Advantage Actor-Critic
Final Thoughts
I hope you found this article insightful. If you're embarking on a new project, I trust this guide will help direct your efforts. I welcome any feedback or thoughts regarding the models I've highlighted—please feel free to leave a comment!
This first video, titled "State of the Art Machine Learning Algorithms for Tabular Data," offers insights into the latest advancements and techniques in this critical area of machine learning.
The second video, "State-of-the-art Machine Learning research with Google tools | Keynote," features key discussions and revelations from the forefront of machine learning research.