Data Augmentation

Augmented SBERT is a data augmentation strategy for pairwise sentence scoring that uses a BERT cross-encoder to improve the performance for the SBERT bi-encoders. Given a pre-trained, well-performing crossencoder, we sample sentence pairs according to a certain sampling strategy and label these using the cross-encoder. We call these weakly labeled examples the silver dataset and they will be merged with the gold training dataset. We then train the bi-encoder on this extended training dataset.

CutBlur is a data augmentation method that is specifically designed for the low-level vision tasks. It cuts a low-resolution patch and pastes it to the corresponding high-resolution image region and vice versa. The key intuition of Cutblur is to enable a model to learn not only "how" but also "where" to super-resolve an image. By doing so, the model can understand "how much" instead of blindly learning to apply super-resolution to every given pixel.

Fast Schema Guided Tracker, or FastSGT, is a fast and robust BERT-based model for state tracking in goal-oriented dialogue systems. The model employs carry-over mechanisms for transferring the values between slots, enabling switching between services and accepting the values offered by the system during dialogue. It also uses multi-head attention projections in some of the decoders to have a better modelling of the encoder outputs. The model architecture is illustrated in the Figure. It consists of four main modules: 1-Utterance Encoder, 2-Schema Encoder, 3-State Decoder, and 4-State Tracker. The first three modules constitute the NLU component and are based on neural networks, whereas the state tracker is a rule-based module. BERT was used for both encoders in the model. The Utterance Encoder is a BERT model which encodes the user and system utterances at each turn. The Schema Encoder is also a BERT model which encodes the schema descriptions of intents, slots, and values into schema embeddings. These schema embeddings help the decoders to transfer or share knowledge between different services by having some language understanding of each slot, intent, or value. The schema and utterance embeddings are passed to the State Decoder - a multi-task module. This module consists of five sub-modules producing the information necessary to track the state of the dialogue. Finally, the State Tracker module takes the previous state along with the current outputs of the State Decoder and predicts the current state of the dialogue by aggregating and summarizing the information across turns.

A generic way of representing and interpolating labels, which allows straightforward extension of any kind of mixup to deep metric learning for a large class of loss functions.

OA-Loss reduces the domain gap between the original and augmented images. OA-Loss enables the model to learn semantic relations of foreground and background instances from multi-domain.

OA-Mix is a general and effective data augmentation method for single-domain generalization in object detection. It increases image diversity while preserving important semantic features with multi-level transformations and object-aware mixing.

XGPT is a method of cross-modal generative pre-training for image captioning designed to pre-train text-to-image caption generators through three novel generation tasks, including image-conditioned masked language modeling (IMLM), image-conditioned denoising autoencoding (IDA), and text-conditioned image feature generation (TIGF). The pre-trained XGPT can be fine-tuned without any task-specific architecture modifications and build strong image captioning models.