内容提示: Are we done with ImageNet?Lucas Beyer 1∗ Olivier J. Hénaff 2∗ Alexander Kolesnikov 1∗ Xiaohua Zhai 1∗ Aäron van den Oord 2∗1 Google Brain (Zürich, CH) and 2 DeepMind (London, UK)AbstractYes, and no. We ask whether recent progress on the ImageNet classif ...
The need for samples can also be greatly reduced by designing a reasonable network structure. Based on the compressed sampling theorem to compress and expand small sample data, we use CNN to directly classify the compressed sampling data features. Compared with the original image input, compressing...
optimized GPU initialization for detection - we use batch=1 initially instead of re-init with batch=1 added correct calculation of mAP, F1, IoU, Precision-Recall using command darknet detector map... added drawing of chart of average-Loss and accuracy-mAP (-map flag) during training run ....
they do not perform as well as diffusion models on image and video generation. To effectively use LLMs for visual generation, one crucial component is the visual tokenizer that maps pixel-space inputs to discrete tokens appropriate for LLM learning. In this paper, we introduce MAGVIT-v2...
"GANs typically work with image data and can use CNNs as the discriminator. But this doesn't work the other way around, meaning a CNN cannot use a GAN," Mead said. One of the biggest challenges is always the data quality itself for training the models, especially when we're talking ab...
To verify whether functional partitions also emerge in FFNs, we propose to convert a model into its MoE version with the same parameters, namely MoEfication. Specifically, MoEfication consists of two phases: (1) splitting the parameters of FFNs into multiple functional partitions as experts, and ...
We ob- served similar data issues in our initial experiments with ”I’m not the cleverest man in the world, but like they say in French: Je ne suis pas un imbecile [I’m not a fool]. In a now-deleted post from Aug. 16, Soheil Eid, Tory candidate in the riding of Joliette, ...
With a vanilla ViT-Huge model, we achieve87.8%accuracy when finetuned on ImageNet-1K. Thisoutperforms all previous results that use only ImageNet-1K data. 具体方法 是时候来谈谈 MAE 的具体方法了。虽然前面铺垫了那么多,但是 CW 认为这是有必要的。教员也告诉我们,看问题要有广度、深度、精度:先纵...
Another thing we see in AlphaFold is the end-to-end goal. In the original AlphaFold, the final assembly of the physical structure was simply driven by the convolutions, and what they came up with. In AlphaFold 2, Jumper and colleagues have emphasized training the neural network from "end ...
A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo. - Effic