Comments (4)
Hi rayleizhu, thanks for your interest in our work!
Yes, you are correct, both I-JEPA and data2vec-(v1,v2) learns representation by trying to predict missing information in feature space. Data2vec-v2 shows that you could learn very strong pretrained models for end2end finetuning on given tasks. In contrast, our goal was to demonstrate that this learning principle also can learns strong off-the-shelf representation that are good in frozen evaluation and low-shot scenario.
This led to different choices in the model architecture (conv decoder vs ViT predictor) and masking strategies. You can find comparisons with data2vec in the paper (data2vec-v2 was not yet released during the development of the paper).
from ijepa.
Hi rayleizhu, thanks for your interest in our work!
Yes, you are correct, both I-JEPA and data2vec-(v1,v2) learns representation by trying to predict missing information in feature space. Data2vec-v2 shows that you could learn very strong pretrained models for end2end finetuning on given tasks. In contrast, our goal was to demonstrate that this learning principle also can learns strong off-the-shelf representation that are good in frozen evaluation and low-shot scenario.
This led to different choices in the model architecture (conv decoder vs ViT predictor) and masking strategies. You can find comparisons with data2vec in the paper (data2vec-v2 was not yet released during the development of the paper).
Thanks for your reply! But I still confused about the so called off-the-shelf representation. Could you please give some detailed explanation or insight on that?
from ijepa.
'off-the-shelf' means that we don't want to finetune the encoder after pretraining, i.e. we froze its parameters after pretraining.
In the paper, we mostly focus on linear evaluation where we train a linear classifier on top of a frozen pretrained encoder. Let me know if that answer you question.
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Yes!And thanks for your kindly reply~
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Related Issues (20)
- Training loss increases HOT 3
- Error in interpolation of pos_embedd when using data of different dimension
- RuntimeError: Default process group has not been initialized, please make sure to call init_process_group. HOT 2
- I Just download the image1k dataset from huggingface, what should i do next to process the tar.gz file and train this model HOT 3
- Is there any visual code?
- How to evaluate the performance of pre-trained model?
- Struggling to replicate evaluation results HOT 14
- An error was reported while training on multiple Gpus HOT 1
- Difficulty continue self supervised pre training on custom dataset HOT 2
- How to load ijepa checkpoints? HOT 6
- Downstream task HOT 5
- Image resolution & folder structure for unsupervised pre-training HOT 1
- imagenet1k Huggingface extraction HOT 1
- Training from scratch HOT 13
- generative model decoder for target-encoder
- what is the difference between root_folder and image_folder? HOT 1
- Struggling to Train Downstream Classifier HOT 5
- Bias in Multiblock Mask Collator
- Why is there no class token? HOT 4
- Suggestions to train on low-end GPU HOT 5
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