Exploring Target Representations for Masked Autoencoders

• URL: http://arxiv.org/abs/2209.03917v3
• Date: Sun, 26 Mar 2023 14:19:16 GMT
• Title: Exploring Target Representations for Masked Autoencoders
• Authors: Xingbin Liu, Jinghao Zhou, Tao Kong, Xianming Lin, Rongrong Ji
• Abstract summary: We show that a careful choice of the target representation is unnecessary for learning good representations. We propose a multi-stage masked distillation pipeline and use a randomly model as the teacher. A proposed method to perform masked knowledge distillation with bootstrapped teachers (dBOT) outperforms previous self-supervised methods by nontrivial margins.
• Score: 78.57196600585462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Masked autoencoders have become popular training paradigms for self-supervised visual representation learning. These models randomly mask a portion of the input and reconstruct the masked portion according to the target representations. In this paper, we first show that a careful choice of the target representation is unnecessary for learning good representations, since different targets tend to derive similarly behaved models. Driven by this observation, we propose a multi-stage masked distillation pipeline and use a randomly initialized model as the teacher, enabling us to effectively train high-capacity models without any efforts to carefully design target representations. Interestingly, we further explore using teachers of larger capacity, obtaining distilled students with remarkable transferring ability. On different tasks of classification, transfer learning, object detection, and semantic segmentation, the proposed method to perform masked knowledge distillation with bootstrapped teachers (dBOT) outperforms previous self-supervised methods by nontrivial margins. We hope our findings, as well as the proposed method, could motivate people to rethink the roles of target representations in pre-training masked autoencoders.The code and pre-trained models are publicly available at https://github.com/liuxingbin/dbot.

Related papers

• Attention-Guided Masked Autoencoders For Learning Image Representations [16.257915216763692]
  Masked autoencoders (MAEs) have established themselves as a powerful method for unsupervised pre-training for computer vision tasks. We propose to inform the reconstruction process through an attention-guided loss function. Our evaluations show that our pre-trained models learn better latent representations than the vanilla MAE.
  arXiv  Detail & Related papers  (2024-02-23T08:11:25Z)
• Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability [70.72426887518517]
  Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. We propose a novel method, Unleashing Mask, which aims to restore the OOD discriminative capabilities of the well-trained model with ID data. Our method utilizes a mask to figure out the memorized atypical samples, and then finetune the model or prune it with the introduced mask to forget them.
  arXiv  Detail & Related papers  (2023-06-06T14:23:34Z)
• Improving Masked Autoencoders by Learning Where to Mask [65.89510231743692]
  Masked image modeling is a promising self-supervised learning method for visual data. We present AutoMAE, a framework that uses Gumbel-Softmax to interlink an adversarially-trained mask generator and a mask-guided image modeling process. In our experiments, AutoMAE is shown to provide effective pretraining models on standard self-supervised benchmarks and downstream tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:28:55Z)
• Masked Autoencoding for Scalable and Generalizable Decision Making [93.84855114717062]
  MaskDP is a simple and scalable self-supervised pretraining method for reinforcement learning and behavioral cloning. We find that a MaskDP model gains the capability of zero-shot transfer to new BC tasks, such as single and multiple goal reaching.
  arXiv  Detail & Related papers  (2022-11-23T07:04:41Z)
• MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis [33.46831766206675]
  MAsked Generative (MAGE) is first framework to unify SOTA image generation and self-supervised representation learning. Inspired by previous generative models, MAGE uses semantic tokens learned by a vector-quantized GAN at inputs and outputs. On ImageNet-1K, a single MAGE ViT-L model obtains 9.10 FID in the task of class-unconditional image generation.
  arXiv  Detail & Related papers  (2022-11-16T18:59:02Z)
• Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders [64.03000385267339]
  Masked image modeling (MIM) has become a popular strategy for self-supervised learning(SSL) of visual representations with Vision Transformers. We present a simple SSL method, the Reconstruction-Consistent Masked Auto-Encoder (RC-MAE) by adding an EMA teacher to MAE. RC-MAE converges faster and requires less memory usage than state-of-the-art self-distillation methods during pre-training.
  arXiv  Detail & Related papers  (2022-10-05T08:08:55Z)
• Self-Distillation for Further Pre-training of Transformers [83.84227016847096]
  We propose self-distillation as a regularization for a further pre-training stage. We empirically validate the efficacy of self-distillation on a variety of benchmark datasets for image and text classification tasks.
  arXiv  Detail & Related papers  (2022-09-30T02:25:12Z)
• Object-wise Masked Autoencoders for Fast Pre-training [13.757095663704858]
  We show that current masked image encoding models learn the underlying relationship between all objects in the whole scene, instead of a single object representation. We introduce a novel object selection and division strategy to drop non-object patches for learning object-wise representations by selective reconstruction with interested region masks. Experiments on four commonly-used datasets demonstrate the effectiveness of our model in reducing the compute cost by 72% while achieving competitive performance.
  arXiv  Detail & Related papers  (2022-05-28T05:13:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.