Multi-View Masked World Models for Visual Robotic Manipulation

Visual robotic manipulation research and applications often use multiple cameras, or views, to better perceive the world. How else can we utilize the richness of multi-view data? In this paper, we investigate how to learn good representations with multi-view data and utilize them for visual robotic manipulation. Specifically, we train a multi-view masked autoencoder which reconstructs pixels of randomly masked viewpoints and then learn a world model operating on the representations from the autoencoder. We demonstrate the effectiveness of our method in a range of scenarios, including multi-view control and single-view control with auxiliary cameras for representation learning. We also show that the multi-view masked autoencoder trained with multiple randomized viewpoints enables training a policy with strong viewpoint randomization and transferring the policy to solve real-robot tasks without camera calibration and an adaptation procedure. Videos demonstrations in real-world experiments and source code are available at the project website: https://sites.google.com/view/mv-mwm.

Efficient Meta-Learning via Error-based Context Pruning for Implicit Neural Representations

We introduce an efficient optimization-based meta-learning technique for learning large-scale implicit neural representations (INRs). Our main idea is designing an online selection of context points, which can significantly reduce memory requirements for meta-learning in any established setting. By doing so, we expect additional memory savings which allows longer per-signal adaptation horizons (at a given memory budget), leading to better meta-initializations by reducing myopia and, more crucially, enabling learning on high-dimensional signals. To implement such context pruning, our technical novelty is three-fold. First, we propose a selection scheme that adaptively chooses a subset at each adaptation step based on the predictive error, leading to the modeling of the global structure of the signal in early steps and enabling the later steps to capture its high-frequency details. Second, we counteract any possible information loss from context pruning by minimizing the parameter distance to a bootstrapped target model trained on a full context set. Finally, we suggest using the full context set with a gradient scaling scheme at test-time. Our technique is model-agnostic, intuitive, and straightforward to implement, showing significant reconstruction improvements for a wide range of signals. Code is available at https://github.com/jihoontack/ECoP

Explaining Visual Biases as Words by Generating Captions

We aim to diagnose the potential biases in image classifiers. To this end, prior works manually labeled biased attributes or visualized biased features, which need high annotation costs or are often ambiguous to interpret. Instead, we leverage two types (generative and discriminative) of pre-trained vision-language models to describe the visual bias as a word. Specifically, we propose bias-to-text (B2T), which generates captions of the mispredicted images using a pre-trained captioning model to extract the common keywords that may describe visual biases. Then, we categorize the bias type as spurious correlation or majority bias by checking if it is specific or agnostic to the class, based on the similarity of class-wise mispredicted images and the keyword upon a pre-trained vision-language joint embedding space, e.g., CLIP. We demonstrate that the proposed simple and intuitive scheme can recover well-known gender and background biases, and discover novel ones in real-world datasets. Moreover, we utilize B2T to compare the classifiers using different architectures or training methods. Finally, we show that one can obtain debiased classifiers using the B2T bias keywords and CLIP, in both zero-shot and full-shot manners, without using any human annotation on the bias.

Modality-Agnostic Variational Compression of Implicit Neural Representations

We introduce a modality-agnostic neural data compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR). Bridging the gap between latent coding and sparsity, we obtain compact latent representations which are non-linearly mapped to a soft gating mechanism capable of specialising a shared INR base network to each data item through subnetwork selection. After obtaining a dataset of such compact latent representations, we directly optimise the rate/distortion trade-off in this modality-agnostic space using non-linear transform coding. We term this method Variational Compression of Implicit Neural Representation (VC-INR) and show both improved performance given the same representational capacity pre quantisation while also outperforming previous quantisation schemes used for other INR-based techniques. Our experiments demonstrate strong results over a large set of diverse data modalities using the same algorithm without any modality-specific inductive biases. We show results on images, climate data, 3D shapes and scenes as well as audio and video, introducing VC-INR as the first INR-based method to outperform codecs as well-known and diverse as JPEG 2000, MP3 and AVC/HEVC on their respective modalities.

Confidence-aware Training of Smoothed Classifiers for Certified Robustness

Any classifier can be "smoothed out" under Gaussian noise to build a new classifier that is provably robust to $\ell_2$-adversarial perturbations, viz., by averaging its predictions over the noise via randomized smoothing. Under the smoothed classifiers, the fundamental trade-off between accuracy and (adversarial) robustness has been well evidenced in the literature: i.e., increasing the robustness of a classifier for an input can be at the expense of decreased accuracy for some other inputs. In this paper, we propose a simple training method leveraging this trade-off to obtain robust smoothed classifiers, in particular, through a sample-wise control of robustness over the training samples. We make this control feasible by using "accuracy under Gaussian noise" as an easy-to-compute proxy of adversarial robustness for an input. Specifically, we differentiate the training objective depending on this proxy to filter out samples that are unlikely to benefit from the worst-case (adversarial) objective. Our experiments show that the proposed method, despite its simplicity, consistently exhibits improved certified robustness upon state-of-the-art training methods. Somewhat surprisingly, we find these improvements persist even for other notions of robustness, e.g., to various types of common corruptions.

OAMixer: Object-aware Mixing Layer for Vision Transformers

Patch-based models, e.g., Vision Transformers (ViTs) and Mixers, have shown impressive results on various visual recognition tasks, alternating classic convolutional networks. While the initial patch-based models (ViTs) treated all patches equally, recent studies reveal that incorporating inductive bias like spatiality benefits the representations. However, most prior works solely focused on the location of patches, overlooking the scene structure of images. Thus, we aim to further guide the interaction of patches using the object information. Specifically, we propose OAMixer (object-aware mixing layer), which calibrates the patch mixing layers of patch-based models based on the object labels. Here, we obtain the object labels in unsupervised or weakly-supervised manners, i.e., no additional human-annotating cost is necessary. Using the object labels, OAMixer computes a reweighting mask with a learnable scale parameter that intensifies the interaction of patches containing similar objects and applies the mask to the patch mixing layers. By learning an object-centric representation, we demonstrate that OAMixer improves the classification accuracy and background robustness of various patch-based models, including ViTs, MLP-Mixers, and ConvMixers. Moreover, we show that OAMixer enhances various downstream tasks, including large-scale classification, self-supervised learning, and multi-object recognition, verifying the generic applicability of OAMixer

Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling

Trying to capture the sample-label relationship, conditional generative models often end up inheriting the spurious correlation in the training dataset, giving label-conditional distributions that are severely imbalanced in another latent attribute. To mitigate such undesirable correlations engraved into generative models, which we call spurious causality, we propose a general two-step strategy. (a) Fairness Intervention (FI): Emphasize the minority samples that are hard to be generated due to the spurious correlation in the training dataset. (b) Corrective Sampling (CS): Filter the generated samples explicitly to follow the desired label-conditional latent attribute distribution. We design the fairness intervention for various degrees of supervision on the spurious attribute, including unsupervised, weakly-supervised, and semi-supervised scenarios. Our experimental results show that the proposed FICS can successfully resolve the spurious correlation in generated samples on various datasets.

Scalable Neural Video Representations with Learnable Positional Features

Succinct representation of complex signals using coordinate-based neural representations (CNRs) has seen great progress, and several recent efforts focus on extending them for handling videos. Here, the main challenge is how to (a) alleviate a compute-inefficiency in training CNRs to (b) achieve high-quality video encoding while (c) maintaining the parameter-efficiency. To meet all requirements (a), (b), and (c) simultaneously, we propose neural video representations with learnable positional features (NVP), a novel CNR by introducing "learnable positional features" that effectively amortize a video as latent codes. Specifically, we first present a CNR architecture based on designing 2D latent keyframes to learn the common video contents across each spatio-temporal axis, which dramatically improves all of those three requirements. Then, we propose to utilize existing powerful image and video codecs as a compute-/memory-efficient compression procedure of latent codes. We demonstrate the superiority of NVP on the popular UVG benchmark; compared with prior arts, NVP not only trains 2 times faster (less than 5 minutes) but also exceeds their encoding quality as 34.07$\rightarrow$34.57 (measured with the PSNR metric), even using $>$8 times fewer parameters. We also show intriguing properties of NVP, e.g., video inpainting, video frame interpolation, etc.

Meta-Learning with Self-Improving Momentum Target

The idea of using a separately trained target model (or teacher) to improve the performance of the student model has been increasingly popular in various machine learning domains, and meta-learning is no exception; a recent discovery shows that utilizing task-wise target models can significantly boost the generalization performance. However, obtaining a target model for each task can be highly expensive, especially when the number of tasks for meta-learning is large. To tackle this issue, we propose a simple yet effective method, coined Self-improving Momentum Target (SiMT). SiMT generates the target model by adapting from the temporal ensemble of the meta-learner, i.e., the momentum network. This momentum network and its task-specific adaptations enjoy a favorable generalization performance, enabling self-improving of the meta-learner through knowledge distillation. Moreover, we found that perturbing parameters of the meta-learner, e.g., dropout, further stabilize this self-improving process by preventing fast convergence of the distillation loss during meta-training. Our experimental results demonstrate that SiMT brings a significant performance gain when combined with a wide range of meta-learning methods under various applications, including few-shot regression, few-shot classification, and meta-reinforcement learning. Code is available at https://github.com/jihoontack/SiMT.

String-based Molecule Generation via Multi-decoder VAE

In this paper, we investigate the problem of string-based molecular generation via variational autoencoders (VAEs) that have served a popular generative approach for various tasks in artificial intelligence. We propose a simple, yet effective idea to improve the performance of VAE for the task. Our main idea is to maintain multiple decoders while sharing a single encoder, i.e., it is a type of ensemble techniques. Here, we first found that training each decoder independently may not be effective as the bias of the ensemble decoder increases severely under its auto-regressive inference. To maintain both small bias and variance of the ensemble model, our proposed technique is two-fold: (a) a different latent variable is sampled for each decoder (from estimated mean and variance offered by the shared encoder) to encourage diverse characteristics of decoders and (b) a collaborative loss is used during training to control the aggregated quality of decoders using different latent variables. In our experiments, the proposed VAE model particularly performs well for generating a sample from out-of-domain distribution.