EfficientNetV2: Convolutional Neural Networks

What is the primary goal of developing EfficientNetV2, as stated in the paper?

What technique is used to adaptively adjust regularization during progressive learning?

What is the reported top-1 accuracy of EfficientNetV2 on ImageNet ILSVRC2012?

What is the benefit of using progressive learning in EfficientNetV2?

What is the reported training speed improvement of EfficientNetV2 compared to previous models?

What is the main purpose of EfficientNetV2, as described in the paper?

What is the main limitation of NFNets and Vision Transformers in terms of training efficiency?

What is the main advantage of EfficientNetV2 compared to other models in terms of training time?

What is the main limitation of EfficientNets in terms of training speed, as identified in the paper?

What is the key approach used in EfficientNetV2 to improve training speed and parameter efficiency?

Introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models.

Training efficiency is important to deep learning as model size and training data size are increasingly larger.
GPT-3 demonstrates the remarkable capability in few shot learning, but it requires weeks of training.

EfficientNetV2 models train much faster than state-of-the-art models while being up to 6.8x smaller.
Training can be further sped up by progressively increasing the image size during training, but it often causes a drop in accuracy.

An improved method of progressive learning, which adaptively adjusts regularization (e.g. data augmentation) along with image size, is proposed to compensate for the accuracy drop.
With progressive learning, EfficientNetV2 significantly outperforms previous models on ImageNet and CIFAR/Cars/Flowers datasets.

EfficientNetV2 achieves 87.3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2.0% accuracy while training 5x-11x faster using the same computing resources.
EfficientNetV2 trains 5x - 11x faster than others, while using up to 6.8x fewer parameters.

NFNets aim to improve training efficiency by removing the expensive batch normalization.
Vision Transformers improve training efficiency on large-scale datasets by using Transformer blocks.
Recent works focus on improving training speed by adding attention layers into convolutional networks (ConvNets).

Learn about EfficientNetV2, a new family of convolutional neural networks with faster training speed and better parameter efficiency.

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