Neural Network Embeddings With Triplet Loss for Viewpoint Invariance
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Solution Overview
Problem
Current neural networks face challenges in generating embeddings that are invariant to transformations such as viewpoint, occlusions, motion-blur, lighting, and background variations, which hinders accurate characterization of environmental states for tasks like robotic manipulation and imitation.
Innovation Solution
A time-contrastive neural network is trained using triplet loss, processing observations from multiple modalities to generate numeric embeddings that differentiate similar observations and are invariant to transformations, by updating network parameters based on gradients of the triplet loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained to generate embeddings for environmental states, then the network can characterize the state for tasks like robotic manipulation, but the embeddings are sensitive to transformations such as viewpoint, occlusions, motion-blur, lighting, and background variations
Solution Approach 1:
The patent implements a contrastive loss function that provides feedback during training by comparing embeddings of the same state under different transformations. The loss function computes distances between anchor embeddings and positive (transformed) embeddings versus negative (different state) embeddings, guiding the network to learn transformation-invariant features through iterative optimization
Solution Approach 2:
The patent applies data augmentation transformations (viewpoint changes, occlusions, motion-blur, lighting variations, background changes) as preliminary actions during the training phase. By pre-exposing the network to these transformations through augmented training samples, the network learns to generate consistent embeddings despite these variations before deployment
2Measurement precision
If the neural network learns to differentiate similar observations captured by the same modality, then the embeddings can accurately characterize environmental states, but the training complexity and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively transforming only certain aspects of the input observations (e.g., applying viewpoint transformations to images but not to other modalities, or applying only a subset of transformations). This reduces the total number of augmented samples and computational operations while still providing sufficient training signal for invariance
Solution Approach 2:
The patent uses a unified contrastive loss function that handles multiple transformation types (viewpoint, occlusion, motion-blur, lighting, background) through a single computational framework. This multi-functional approach consolidates what would otherwise require separate training procedures into one efficient process
Data Source
AI summary
This description relates to a neural network that has multiple network parameters and is configured to receive an input observation characterizing a state of an environment and to process the input observation to generate a numeric embedding of the state of the environment. The neural network can be used to control a robotic agent. The network can be trained using a method comprising: obtaining a first observation captured by a first modality; obtaining a second observation that is co-occurring with the first observation and that is captured by a second, different modality; obtaining a third observation captured by the first modality that is not co-occurring with the first observation; determining a gradient of a triplet loss that uses the first observation, the second observation, and the third observation; and updating current values of the network parameters using the gradient of the triplet loss.