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Context Encoders: Feature Learning by Inpainting

Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros

arXiv:1604.07379v2cs.CVcs.AIcs.GRcs.LG

TL;DR

Learning visual structure from incomplete images is difficult because missing regions require semantic understanding. Context Encoders predict missing image content from surrounding pixels and learn features competitive across classification, detection, segmentation, and inpainting tasks.

  • Problem

    Existing autoencoder-style objectives may learn compressed appearance without requiring the semantic understanding needed to predict large missing image regions.

  • Method

    Context Encoders use a convolutional encoder-decoder to predict arbitrary missing image regions from surrounding context, with optional adversarial loss for sharper outputs.

  • Results

    Context Encoders learn semantically meaningful features competitive with unsupervised methods across classification, detection, and segmentation, while advancing semantic inpainting.

  • Takeaways & Limitations

    Context-based pixel prediction can provide useful visual representations while producing realistic image completions for semantic inpainting.

  • Takeaways & Limitations

    Central-square masking can make features latch onto mask boundaries, reducing their generalization to images without masks.

Abstract

from arXiv · show

We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders -- a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods.

1. Introduction

The paper introduces context encoders, unsupervised CNNs that infer missing image regions from surrounding context through compact latent representations. It argues that this task promotes semantic visual features and evaluates the learned representations on image understanding tasks.

  • Motivation: The work explores whether convolutional neural networks can learn and predict the structured visual content that humans infer from surrounding image pixels.The motivation is the human ability to imagine or draw missing image regions from visible context.
  • Method: Context encoders use an encoder to summarize image context into a compact latent representation and a decoder to generate missing content.The model is closely related to autoencoders but predicts an absent region rather than reconstructing the complete input.
  • Motivation: Unlike localized corruption in denoising autoencoders, missing-region prediction is intended to encourage semantically meaningful visual representations.The introduction contrasts context encoders with autoencoders that may merely compress image content and denoising autoencoders that typically use localized, low-level corruption.
  • Method: The inpainting task requires understanding the image while producing a plausible hypothesis for missing content, which can have multiple coherent solutions.The authors describe the task as inherently multi-modal and address this through the training loss.
  • Evaluation: The authors validate learned features by retrieving semantically similar unseen patches and fine-tuning encoders for classification, object detection, and semantic segmentation.The encoder and decoder are evaluated independently, with nearest-neighbor context retrieval used to assess semantic similarity.

2. Related work

Related work spans unsupervised representation learning, spatial-context supervision, image generation, and inpainting. Context encoders are distinguished by predicting missing pixel intensities from surrounding context rather than solving a discriminative spatial task, while combining reconstruction with adversarial training for inpainting.

  • Unsupervised learning: Unsupervised CNN features trained with ImageNet labels generalize across tasks, but learning equally semantic and generalizable features from raw images without labels remains open.Autoencoders and denoising autoencoders are identified as early deep unsupervised-learning approaches.
  • Related spatial-context methods: Spatial-context methods used context to predict masked objects or establish correspondences, but relied on hand-designed features rather than representation learning.Visual Memex modeled object relations nonparametrically, while another approach used context for unsupervised object discovery.
  • Spatial-context supervision: Unlike prior discriminative spatial prediction, context encoders solve a pure prediction problem: determining which pixel intensities belong in the missing region.The paper contrasts deciding whether patch A is above or below patch B with predicting the contents of the hole.
  • Image generation: Context encoders use an adversary jointly with reconstruction loss to generate inpainting results, building on recent progress in convolutional GAN architectures.The paper places this training approach within broader research on generative models of natural images.
  • Image generation: Unlike category-specific image generators trained on large labeled datasets, context encoders generate images from surrounding context using any unlabeled image database.Prior CNN generators demonstrated novel images for chairs and faces, respectively.
  • Inpainting and hole-filling: Classical inpainting and texture synthesis are inadequate for the paper’s large missing regions, whereas scene completion typically uses nearest-neighbor cut-and-paste from millions of images.The paper characterizes classical local methods as non-semantic and scene completion as a dataset-based alternative.

3. Context encoders for image generation

Context encoders predict missing image regions from surrounding pixels using an encoder-decoder architecture with a channel-wise fully connected connection. Training combines reconstruction and adversarial objectives, while region-dropout strategies are chosen to support both inpainting and general feature learning.

  • Architecture: Context encoders use an encoder-decoder pipeline that maps masked images to latent features and generates the missing content.The encoder and decoder are connected through a channel-wise fully connected layer so decoder units can reason about the entire image.
  • Decoder: The decoder connects encoder features through a channel-wise fully connected layer before generating pixels with learned up-convolutions.The up-convolutional layers increase image resolution and use ReLU activations.
  • Training objective: The normalized masked L2 reconstruction loss captures coarse structure but often produces blurry predictions lacking high-frequency detail.Experiments found no significant difference between L1 and L2 losses, while either loss can favor safer blurry solutions over accurate textures.
  • Training objective: An adversarial loss is added to model multiple plausible completions and encourage the entire context-encoder output to look realistic.The adversarial framework jointly optimizes a generator, identified with the context encoder, and a discriminator.
  • Region removal: Central masks support inpainting but induce boundary-specific low-level features that generalize poorly, whereas region and random-block masks produce similarly general features.Random region dropout is used for feature-based experiments because it significantly outperforms central-region features.

4. Implementation details

The implementation uses Caffe and Torch with ADAM optimization and mean-value filling for masked regions. Pool-free encoders produce finer inpainting, but AlexNet with pooling is retained for feature-learning comparisons.

  • Implementation: The pipeline was implemented in Caffe and Torch, optimized with ADAM, and initialized by filling masked regions with the constant mean value.Hyper-parameter details are deferred to Sections 5.1 and 5.2.
  • Pool-free encoders: Replacing pooling layers with same-kernel, same-stride convolutions preserves overall stride while producing finer inpainting.The authors motivate pool-free encoders because pooling is unnecessary for reconstruction-based networks.
  • Feature learning: Although pooling may harm reconstruction-based training by reducing spatial sensitivity, the original pooling-based AlexNet is retained for consistency with prior feature-learning work.Pooling provides spatial invariance in classification, but that property may be detrimental for reconstruction.

5. Evaluation

The evaluation tests context-encoder features for semantic inpainting and transferability across classification, detection, and segmentation. Results show semantically aligned inpainting and competitive or improved downstream performance relative to several baselines.

  • Semantic inpainting: Context encoders generally inpaint semantic image regions well, while texture-synthesis methods can perform better when missing regions contain only low-level textures.The comparison includes nearest-neighbor inpainting as a baseline for semantic inpainting.
  • Semantic inpainting: Joint reconstruction and adversarial loss significantly improves inpainting over either reconstruction or adversarial loss alone.The joint loss combines the strengths of sharper adversarial outputs and better coherence from reconstruction.
  • Context nearest neighbors: Context features retrieve decent nearest neighbors for masked content from surrounding context, whereas HOG fails to capture the relevant semantics.The center content is withheld from both query and dataset images during retrieval.
  • Transfer learning: On PASCAL VOC 2007 classification, context encoders are competitive with concurrent self-supervised methods and significantly outperform autoencoders and Agrawal et al.A random initialization performs roughly 25% below an ImageNet-trained model without using labels.
  • Transfer learning: For PASCAL VOC 2007 detection, context-encoder pre-training is competitive with existing methods and provides a significant boost over the baseline.The detector uses Fast R-CNN and reports accuracy using mean average precision.
  • Transfer learning: On PASCAL VOC 2012 semantic segmentation, context encoders outperform both random initialization and a plain autoencoder trained to reconstruct the full input.The evaluation follows the FCN training and evaluation procedure for direct comparison.

6. Conclusion

The paper concludes that context encoders advance semantic inpainting while learning feature representations competitive with models trained using auxiliary supervision.

  • 6. Conclusion: Context encoders advance the state of the art in semantic inpainting while learning competitive feature representations.The representations are competitive with other models trained with auxiliary supervision.

Supplementary Material

The supplementary material details the context-encoder architecture and provides additional qualitative results, including semantic inpainting and transferable features.

  • Supplementary Material: The supplementary material covers context-encoder architecture, additional qualitative results, semantic inpainting, and feature transfer to other tasks.Implementation details for these topics are presented in subsequent sections.

A. Semantic Inpainting

Semantic inpainting uses context encoders trained with joint reconstruction and adversarial losses to generate missing image regions. The adversarial discriminator compares complete real and generated images without conditioning on the mask, preventing trivial boundary-based classification.

  • Training setup: Context encoders for inpainting are trained jointly with reconstruction and adversarial losses on images resized to 128 × 128.Using 227 × 227 images directly produced slightly worse inpainting results.
  • Training setup: The encoder and discriminator resemble the discriminator architecture, while the decoder resembles the generator architecture, in [33].The bottleneck contains 4000 units, and batch normalization was used in both the context encoder.
  • Adversarial discrimination: For arbitrary-region inpainting, the adversarial discriminator compares the full real image with the full generated image without receiving the mask.Mask conditioning would expose perceptual discontinuities between generated and real regions, allowing easy classification and preventing training.
  • Adversarial discrimination: For center-region inpainting, this full-image discrimination process can be computationally simplified.The passage states this simplification specifically for center-region inpainting.

B. Feature Learning

The feature-learning model uses an AlexNet-based encoder through pool5 and a channel-wise fully connected layer, followed by an upconvolutional decoder. Images are 227 × 227, and the AlexNet encoder was trained with reconstruction loss because adversarial training was unsuccessful.

  • Architecture: The encoder follows AlexNet through pool5, enabling comparison with prior supervised and unsupervised feature-learning methods.The cited passage states that prior methods used ImageNet labels and other un/self-supervised techniques.
  • Architecture: A channel-wise fully connected layer follows the encoder, while the decoder uses upconvolutional layers to reach the target size.
  • Training setup: The model takes input images of size 227 × 227.
  • Training setup: Because adversarial training could not be performed with the AlexNet encoder, it was trained with reconstruction loss.

C. Additional Results

Additional experiments show that context encoders learn semantically meaningful features for context-based patch retrieval and produce realistic, coherent inpainting that outperforms nearest-neighbor methods. Joint reconstruction and adversarial training combines better alignment and sharpness while alleviating their individual weaknesses.

  • Additional Results: Context-encoder features are semantically meaningful, enabling neighboring patches to be retrieved from surrounding context alone.This finding is also verified quantitatively in Table 2.
  • Additional Results: Context encoders produce realistic, coherent inpainting without memorizing training examples and outperform nearest-neighbor inpainting qualitatively and quantitatively.The comparison is reported in Figure 10 and Table 1.
  • Additional Results: Joint reconstruction and adversarial loss alleviates the weaknesses of reconstruction-only and adversarial-only inpainting, combining alignment with sharper results.Reconstruction-only outputs are aligned but not sharp, whereas adversarial-only outputs are sharp but not coherent.
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