Tag: ImageNet

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Alibaba’s USI: A Unified Scheme for Training Any Backbone on ImageNet That Delivers Top Results Without Hyperparameter Tuning

In the new paper Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results, a research team from Alibaba Group’s DAMO Academy introduces USI (Unified Scheme for ImageNet), a unified scheme for training any backbone on ImageNet that does not require adjustments or hyperparameter tuning between different models, and consistently yields top model results in terms of accuracy and efficiency.

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Pushing the Limits of Self-Supervised ResNets: DeepMind’s ReLICv2 Beats Strong Supervised Baselines on ImageNet

A DeepMind research team proposes ReLICv2, which demonstrates for the first time that representations learned without labels can consistently outperform a strong, supervised baseline on ImageNet and even achieve comparable results to state-of-the-art self-supervised vision transformers (ViTs).

AI Computer Vision & Graphics Machine Learning & Data Science Research

Google & Rutgers’ Aggregating Nested Transformers Yield Better Accuracy, Data Efficiency and Convergence

A research team from Google Cloud AI, Google Research and Rutgers University simplifies vision transformers’ complex design, proposing nested transformers (NesT) that simply stack basic transformer layers to process non-overlapping image blocks individually. The approach achieves superior ImageNet classification accuracy and improves model training efficiency.

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OpenAI Founder: Short-Term AGI Is a Serious Possibility

Artificial general intelligence (AGI) is the long-range, human-intelligence-level target of contemporary AI researchers worldwide. It’s believed AGI has the potential to meet basic human needs globally, end poverty, cure diseases, extend life, and even mitigate climate change. In short, AGI is the tech that could not only save the world, but build a utopia.